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v2024-beta
Daftar isi
Kami menggunakan proyek lain untuk secara otomatis melacak pembaruan pada kertas FL, klik pada FL-paper-update-tracker jika Anda membutuhkannya.
Lebih banyak item akan ditambahkan ke repositori . Silakan menyarankan sumber daya penting lainnya dengan membuka laporan masalah, mengirimkan permintaan penarikan, atau mengirimi saya email @ ([email protected]). Jika ingin berkomunikasi lebih banyak dengan teman di bidang pembelajaran gabungan, silakan bergabung dengan grup QQ [联邦学习交流群], nomor grup 833638275. Selamat membaca!
Pemberitahuan Pembaruan Repositori
30/09/2024
Pengguna yang terhormat, Kami ingin memberi tahu Anda tentang beberapa perubahan yang akan memengaruhi repositori sumber terbuka ini. Pemilik dan kontributor utama @youngfish42 telah berhasil menyelesaikan studi doktoralnya? per 30 September 2024, dan sejak itu mengalihkan fokus penelitiannya. Perubahan keadaan ini akan berdampak pada frekuensi dan tingkat pembaruan pada daftar makalah repositori.
Dibandingkan dengan pembaruan rutin sebelumnya, kami mengantisipasi bahwa daftar makalah sekarang akan diperbarui setiap bulan atau setiap triwulan. Selain itu, kedalaman pembaruan ini akan dikurangi. Misalnya, pembaruan terkait institusi pembuat dan kode sumber terbuka tidak lagi dipertahankan secara aktif.
Kami memahami bahwa hal ini mungkin memengaruhi nilai yang Anda peroleh dari repositori ini. Oleh karena itu, kami dengan rendah hati mengundang lebih banyak kontributor untuk ikut serta memperbarui konten. Upaya kolaboratif ini akan memastikan bahwa repositori tetap menjadi sumber daya yang berharga bagi semua orang.
Kami menghargai pengertian Anda dan menantikan dukungan dan kontribusi Anda yang berkelanjutan.
Salam,
白小鱼 (ikan muda)
kategori
Kecerdasan Buatan (IJCAI, AAAI, AISTATS, ALT, AI)
Pembelajaran Mesin (NeurIPS, ICML, ICLR, COLT, UAI, Pembelajaran Mesin, JMLR, TPAMI)
Penambangan Data (KDD, WSDM)
Aman (S&P, CCS, Keamanan USENIX, NDSS)
Visi Komputer (ICCV, CVPR, ECCV, MM, IJCV)
Pemrosesan Bahasa Alami (ACL, EMNLP, NAACL, COLING)
Pengambilan Informasi (SIGIR)
Basis Data (SIGMOD, ICDE, VLDB)
Jaringan (SIGCOMM, INFOCOM, MOBICOM, NSDI, WWW)
Sistem (OSDI, SOSP, ISCA, MLSys, EuroSys, TPDS, DAC, TOCS, TOS, TCAD, TC)
Lainnya (ICSE, FOCS, STOC)
| Lokasi | 2024-2020 | sebelum tahun 2020 |
|---|---|---|
| IJCAI | 24, 23, 22, 21, 20 | 19 |
| AAAI | 24, 23, 22, 21, 20 | - |
| AISTAT | 24, 23, 22, 21, 20 | - |
| ALT | 22 | - |
| AI (J) | 23 | - |
| sarafIPS | 24, 23, 22, 21, 20 | 18, 17 |
| ICML | 24, 23, 22, 21, 20 | 19 |
| ICLR | 24, 23, 22, 21, 20 | - |
| KUDA JANTAN MUDA | 23 | - |
| UAI | 23, 22, 21 | - |
| Pembelajaran Mesin (J) | 24, 23, 22 | - |
| JMLR (J) | 24, 23, 22 | - |
| TPAMI (J) | 25, 24, 23, 22 | - |
| KDD | 24, 23, 22, 21, 20 | |
| WSDM | 24, 23, 22, 21 | 19 |
| S&P | 24, 23, 22 | 19 |
| CCS | 24, 23, 22, 21, 19 | 17 |
| Keamanan USENIX | 23, 22, 20 | - |
| NDSS | 24, 23, 22, 21 | - |
| CVPR | 24, 23, 22, 21 | - |
| ICCV | 23,21 | - |
| ECCV | 24, 22, 20 | - |
| MM | 24, 23, 22, 21, 20 | - |
| IJCV (J) | 24 | - |
| ACL | 23, 22, 21 | 19 |
| NAACL | 24, 22, 21 | - |
| EMNLP | 24, 23, 22, 21, 20 | - |
| PENDINGINAN | 20 | - |
| SIGIR | 24, 23, 22, 21, 20 | - |
| SIGMOD | 22, 21 | - |
| ICDE | 24, 23, 22, 21 | - |
| VLDB | 23, 22, 21, 21, 20 | - |
| SIGCOMM | - | - |
| INFOCOM | 24, 23, 22, 21, 20 | 19, 18 |
| MobiCom | 24, 23, 22, 21, 20 | |
| NSDI | 23(1, 2) | - |
| WWW | 24, 23, 22, 21 | |
| OSDI | 21 | - |
| SOSP | 21 | - |
| ISCA | 24 | - |
| MLSys | 24, 23, 22, 20 | 19 |
| EuroSys | 24, 23, 22, 21, 20 | |
| TPDS (J) | 24, 23, 22, 21, 20 | - |
| DAC | 24, 22, 21 | - |
| Daftar Isi | - | - |
| KL | - | - |
| TCAD | 24, 23, 22, 21 | - |
| karena | 24, 23, 22, 21 | - |
| ICSE | 23, 21 | - |
| FOKUS | - | - |
| STOK | - | - |
kata kunci
Statistik: kode tersedia & bintang >= 100 | kutipan >= 50 | ? Tempat tingkat atas
kg. : Grafik Pengetahuan | data. : kumpulan data | surv. : survei
Makalah pembelajaran gabungan di Alam (dan sub-jurnalnya), Sel, Sains (dan Kemajuan Sains) dan PANS mengacu pada mesin pencari WOS.
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| MatSwarm: komputasi materi berbasis pembelajaran transfer kawanan yang tepercaya untuk berbagi data besar yang aman | USTB; NTU | Nat. Komunitas. | 2024 | [PUB] [KODE] |
| Memperkenalkan kecerdasan edge ke smart meter melalui pembelajaran terpisah gabungan | HKU | Nat. Komunitas. | 2024 | [PUB] [新闻] |
| Sebuah studi internasional yang menghadirkan platform pembelajaran AI gabungan untuk tumor otak anak | Universitas Stanford | Nat. Komunitas. | 2024 | [PUB] [KODE] |
| PPML-Omics: Metode pembelajaran mesin gabungan yang menjaga privasi melindungi privasi pasien dalam data omic | KAUST | Kemajuan Ilmu Pengetahuan | 2024 | [PUB] [KODE] |
| Pembelajaran gabungan bukanlah solusi terbaik untuk etika data | TUMP; UVA | Nat. Mach. Intell.(Komentar) | 2024 | [PUB] |
| Model pembelajaran gabungan yang kuat untuk mengidentifikasi pasien berisiko tinggi dengan kekambuhan kanker lambung pasca operasi | Rumah Sakit Pusat Jiangmen; Universitas Teknologi Dirgantara Guilin; Universitas Teknologi Elektronik Guilin; | Nat. Komunitas. | 2024 | [PUB] [KODE] |
| Berbagi pengetahuan selektif untuk penyulingan gabungan yang menjaga privasi tanpa guru yang baik | HKUST | Nat. Komunitas. | 2024 | [PUB] [PDF] [KODE] |
| Sistem pembelajaran gabungan untuk onkologi presisi di Eropa: DigiONE | Penelitian Kanker IQVIA BV | Nat. medis. (Komentar) | 2024 | [PUB] |
| Komputasi kuantum buta terdistribusi multi-klien dengan arsitektur Qline | Sapienza Università di Roma | Nat. Komunitas. | 2023 | [PUB] [PDF] |
| Keacakan kuantum yang tidak bergantung pada perangkat – bukti tanpa pengetahuan yang ditingkatkan | USTC | PNAS | 2023 | [PUB] [PDF] [新闻] |
| Penyortiran baterai pensiunan yang kolaboratif dan menjaga privasi untuk daur ulang langsung yang menguntungkan melalui pembelajaran mesin gabungan | Universitas Tsinghua | Nat. Komunitas. | 2023 | [PUB] |
| Mengadvokasi privasi neurodata dan regulasi neuroteknologi | Universitas Kolombia | Nat. protokol. (Perspektif) | 2023 | [PUB] |
| Pembandingan gabungan kecerdasan buatan medis dengan MedPerf | IHU Strasbourg; Universitas Strasbourg; Institut Kanker Dana-Farber; Kedokteran Weill Cornell; Sekolah Kesehatan Masyarakat Harvard TH Chan; MIT; Intel | Nat. Mach. Intel. | 2023 | [PUB] [PDF] [KODE] |
| Keadilan algoritmik dalam kecerdasan buatan untuk kedokteran dan perawatan kesehatan | Sekolah Kedokteran Harvard; Institut Luas Harvard dan Institut Teknologi Massachusetts; Institut Kanker Dana-Farber | Nat. Bioma. bahasa Inggris (Perspektif) | 2023 | [PUB] [PDF] |
| Transfer pengetahuan privat yang berbeda untuk pembelajaran gabungan | KAMIS | Nat. Komunitas. | 2023 | [PUB] [KODE] |
| Pembelajaran gabungan yang terdesentralisasi melalui berbagi model proksi | Lapisan 6 AI; Universitas Waterloo; Institut Vektor | Nat. Komunitas. | 2023 | [PUB] [PDF] [KODE] |
| Pembelajaran mesin gabungan dalam penelitian yang sesuai dengan perlindungan data | Universitas Hamburg | Nat. Mach. Intell.(Komentar) | 2023 | [PUB] |
| Pembelajaran gabungan untuk memprediksi respon histologis terhadap kemoterapi neoadjuvan pada kanker payudara triple-negatif | Okin | Nat. medis. | 2023 | [PUB] [KODE] |
| Pembelajaran gabungan memungkinkan data besar untuk mendeteksi batas kanker yang langka | Universitas Pennsylvania | Nat. Komunitas. | 2022 | [PUB] [PDF] [KODE] |
| Pembelajaran gabungan dan kedaulatan data genom Pribumi | Memeluk Wajah | Nat. Mach. Intel. (Komentar) | 2022 | [PUB] |
| Pembelajaran representasi terurai gabungan untuk deteksi anomali otak tanpa pengawasan | TUMP | Nat. Mach. Intel. | 2022 | [PUB] [PDF] [KODE] |
| Mengalihkan pembelajaran mesin untuk layanan kesehatan dari pengembangan ke penerapan dan dari model ke data | Universitas Stanford; Biosains Greenstone | Nat. Bioma. bahasa Inggris (Ulasan Artikel) | 2022 | [PUB] |
| Kerangka kerja jaringan neural grafik gabungan untuk personalisasi yang menjaga privasi | KAMIS | Nat. Komunitas. | 2022 | [PUB] [KODE] [解读] |
| Pembelajaran gabungan yang efisien komunikasi melalui penyulingan pengetahuan | KAMIS | Nat. Komunitas. | 2022 | [PUB] [PDF] [KODE] |
| Pimpin pembelajaran neuromorfik gabungan untuk kecerdasan buatan tepi nirkabel | XMU; NTU | Nat. Komunitas. | 2022 | [PUB] [KODE] [解读] |
| Pendekatan pembelajaran gabungan terdesentralisasi yang baru untuk melatih data medis swasta yang didistribusikan secara global, berkualitas buruk, dan terlindungi | Universitas Wollongong | Sains. Reputasi. | 2022 | [PUB] |
| Meningkatkan diagnosis COVID-19 dengan kolaborasi menjaga privasi dalam kecerdasan buatan | HARUS | Nat. Mach. Intel. | 2021 | [PUB] [PDF] [KODE] |
| Pembelajaran gabungan untuk memprediksi hasil klinis pada pasien COVID-19 | Radiologi MGH dan Harvard Medical School | Nat. medis. | 2021 | [PUB] [KODE] |
| Intervensi permusuhan dan mitigasinya dalam pembelajaran mesin kolaboratif yang menjaga privasi | Perguruan Tinggi Kekaisaran London; TUMP; Terbuka | Nat. Mach. Intell.(Perspektif) | 2021 | [PUB] |
| Swarm Learning untuk pembelajaran mesin klinis yang terdesentralisasi dan rahasia | DZNE; Universitas Bonn; | Alam ? | 2021 | [PUB] [KODE] [PERANGKAT LUNAK] [解读] |
| Privasi menyeluruh menjaga pembelajaran mendalam tentang pencitraan medis multi-institusi | TUMP; Perguruan Tinggi Kekaisaran London; Terbuka | Nat. Mach. Intel. | 2021 | [PUB] [KODE] [解读] |
| Pembelajaran gabungan yang efisien dalam komunikasi | CUHK; Universitas Princeton | PANS. | 2021 | [PUB] [KODE] |
| Mendobrak batasan pembagian data medis dengan menggunakan radiografi yang disintesis | Universitas RWTH Aachen | Sains. Kemajuan. | 2020 | [PUB] [KODE] |
| Pembelajaran mesin yang aman, menjaga privasi, dan terpadu dalam pencitraan medis | TUMP; Perguruan Tinggi Kekaisaran London; Terbuka | Nat. Mach. Intell.(Perspektif) | 2020 | [PUB] |
Makalah Pembelajaran Federasi diterima oleh konferensi dan jurnal AI (Kecerdasan Buatan) terkemuka, Termasuk IJCAI (Konferensi Gabungan Internasional tentang Kecerdasan Buatan), AAAI (Konferensi AAAI tentang Kecerdasan Buatan), AISTATS (Kecerdasan Buatan dan Statistik), ALT (Konferensi Internasional tentang Pembelajaran Algoritma) Teori), AI (Kecerdasan Buatan).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| Pengelompokan Multi-Tampilan Federasi melalui Faktorisasi Tensor | IJCAI | 2024 | [PUB] | |
| Pengelompokan Multi-Tampilan Federasi yang Efisien dengan Faktorisasi Matriks Terintegrasi dan K-Means | IJCAI | 2024 | [PUB] | |
| LG-FGAD: Kerangka Deteksi Anomali Grafik Federasi yang Efektif | IJCAI | 2024 | [PUB] | |
| Pembelajaran Cepat Terfederasi untuk Model Weather Foundation di Perangkat | IJCAI | 2024 | [PUB] | |
| Mendobrak Hambatan Heterogenitas Sistem: Pembelajaran Federasi Multimodal yang Toleran Tertinggal melalui Distilasi Pengetahuan | IJCAI | 2024 | [PUB] | |
| Berhenti Belajar selama Pembelajaran: Metode Penghentian Pembelajaran Mesin Federasi yang Efisien | IJCAI | 2024 | [PUB] | |
| Kompresi Gradien Hibrid Praktis untuk Sistem Pembelajaran Federasi | IJCAI | 2024 | [PUB] | |
| Penemuan Kausal Gabungan Sadar Heterogenitas Kualitas Sampel melalui Pemilihan Ruang Variabel Adaptif | IJCAI | 2024 | [PUB] [KODE] | |
| Pembelajaran Federasi yang Teratur Norma Fitur: Memanfaatkan Disparitas Data untuk Peningkatan Performa Model | IJCAI | 2024 | [PUB] [KODE] | |
| Kuantifikasi Ketidakpastian Berbasis Dirichlet untuk Pembelajaran Federasi yang Dipersonalisasi dengan Jaringan Posterior yang Lebih Baik | IJCAI | 2024 | [PUB] | |
| FedConPE: Bandit Percakapan Federasi yang Efisien dengan Klien Heterogen | IJCAI | 2024 | [PUB] | |
| DarkFed: Serangan Pintu Belakang Bebas Data dalam Pembelajaran Federasi | IJCAI | 2024 | [PUB] | |
| Penghentian Pembelajaran Federasi yang Dapat Diskalakan melalui Sharding Terisolasi dan Berkode | IJCAI | 2024 | [PUB] | |
| Meningkatkan Rekomendasi Lintas Domain Target Ganda dengan Pembelajaran Pelestarian Privasi Terfederasi | IJCAI | 2024 | [PUB] | |
| Kebocoran Label dalam Pembelajaran Federasi Vertikal: Sebuah Survei | IJCAI | 2024 | [PUB] | |
| Bangkitnya Kecerdasan Federasi: Dari Model Yayasan Federasi Menuju Kecerdasan Kolektif | IJCAI | 2024 | [PUB] | |
| LEAP: Optimasi Hierarchical Federated Learning pada Data Non-IID dengan Game Formasi Koalisi | IJCAI | 2024 | [PUB] | |
| EAB-FL: Memperburuk Bias Algoritmik melalui Serangan Keracunan Model dalam Pembelajaran Federasi | IJCAI | 2024 | [PUB] | |
| Penyulingan Pengetahuan dalam Pembelajaran Federasi: Panduan Praktis | IJCAI | 2024 | [PUB] | |
| FedGCS: Kerangka Generatif untuk Seleksi Klien yang Efisien dalam Pembelajaran Federasi melalui Optimasi Berbasis Gradien | IJCAI | 2024 | [PUB] | |
| FedPFT: Penyempurnaan Proxy Federasi pada Model Fondasi | IJCAI | 2024 | [PUB] [KODE] | |
| Survei Sistematis tentang Pembelajaran Semi-supervisi Federasi | IJCAI | 2024 | [PUB] | |
| Agen Cerdas untuk Pembelajaran Federasi Berbasis Lelang: Sebuah Survei | IJCAI | 2024 | [PUB] | |
| Strategi Penawaran Memaksimalkan Pendapatan Bebas Bias untuk Konsumen Data dalam Pembelajaran Federasi Berbasis Lelang | IJCAI | 2024 | [PUB] | |
| Pembelajaran Federasi Personalisasi Berbasis Kalibrasi Ganda | IJCAI | 2024 | [PUB] | |
| Dukungan Keputusan yang berorientasi pada pemangku kepentingan untuk Pembelajaran Federasi berbasis Lelang | IJCAI | 2024 | [PUB] | |
| Mendefinisikan Ulang Kontribusi: Pembelajaran Federasi Berbasis Shapley | IJCAI | 2024 | [PUB] [KODE] | |
| Survei tentang Metode Pembelajaran Federasi yang Efisien untuk Pelatihan Model Fondasi | IJCAI | 2024 | [PUB] | |
| Dari Pengoptimalan ke Generalisasi: Pembelajaran Federasi yang Adil melawan Pergeseran Kualitas melalui Pencocokan Ketajaman Antar-Klien | IJCAI | 2024 | [PUB] [KODE] | |
| FBLG: Pendekatan Berbasis Grafik Lokal untuk Menangani Data Non-IID Kemiringan Ganda dalam Pembelajaran Federasi | IJCAI | 2024 | [PUB] | |
| FedFa: Paradigma Pelatihan Sepenuhnya Asinkron untuk Pembelajaran Federasi | IJCAI | 2024 | [PUB] | |
| FedSSA: Agregasi Berbasis Kesamaan Semantik untuk Pembelajaran Federasi Personalisasi Model-Heterogen yang Efisien | IJCAI | 2024 | [PUB] | |
| FedES: Penghentian Dini Federasi untuk Menghambat Penghafalan Kebisingan Label Heterogen | IJCAI | 2024 | [PUB] | |
| Pembelajaran Federasi yang Dipersonalisasi untuk Prediksi Lalu Lintas Lintas Kota | IJCAI | 2024 | [PUB] | |
| Adaptasi Federasi untuk Rekomendasi Berbasis Model Fondasi | IJCAI | 2024 | [PUB] | |
| BADFSS: Serangan Pintu Belakang pada Pembelajaran Mandiri yang Diawasi Terfederasi | IJCAI | 2024 | [PUB] | |
| Memperkirakan sebelum Debiasing: Pendekatan Bayesian untuk Melepaskan Bias Sebelumnya dalam Pembelajaran Semi-Supervisi Federasi | IJCAI | 2024 | [PUB] [KODE] | |
| FedTAD: Distilasi Pengetahuan Bebas Data yang Sadar Topologi untuk Pembelajaran Federasi Subgraf | IJCAI | 2024 | [PUB] | |
| BOBA: Pembelajaran Federasi yang Kuat Bizantium dengan Label Skewness | UIUC | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Bandit Kontekstual Linier Federasi dengan Klien Heterogen | Universitas Virginia | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Desain Eksperimen Terfederasi dalam Privasi Diferensial Terdistribusi | Universitas Stanford; Meta | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Melarikan diri dari Saddle Point dalam Pembelajaran Federasi Heterogen melalui SGD Terdistribusi dengan Kompresi Komunikasi | Universitas Princeton | AISTAT | 2024 | [PUB] [PDF] |
| SGD Asinkron pada Grafik: Kerangka Kerja Terpadu untuk Optimasi Terdesentralisasi dan Federasi Asinkron | INRIA | AISTAT | 2024 | [PUB] [PDF] |
| SIFU: Penghentian Pembelajaran Federasi yang Diinformasikan Secara Berurutan untuk Penghentian Pembelajaran Klien yang Efisien dan Terbukti dalam Pengoptimalan Federasi | INRIA | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Kompresi dengan Distribusi Kesalahan Tepat untuk Pembelajaran Federasi | Politeknik Ecole | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Optimasi Minimax Federasi Adaptif dengan Kompleksitas Lebih Rendah | NJU; Laboratorium Kunci MIIT untuk Analisis Pola dan Kecerdasan Mesin | AISTAT | 2024 | [PUB] [PDF] |
| Kompresi Adaptif dalam Pembelajaran Federasi melalui Informasi Samping | Universitas Stanford; Universitas Padova | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Pembelajaran Federasi Sesuai Permintaan untuk Distribusi Kelas Target Sewenang-wenang | UNIS | AISTAT | 2024 | [PUB] [KODE] |
| FedFisher: Memanfaatkan Informasi Fisher untuk Pembelajaran Federasi Sekali Pakai | CMU | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Antrian dinamika Pembelajaran Federasi asinkron | Huawei | AISTAT | 2024 | [PUB] [PDF] |
| Bandit Bersenjata X Federasi yang Dipersonalisasi | Universitas Purdue | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Pembelajaran Federasi Untuk Catatan Kesehatan Elektronik Heterogen Memanfaatkan Jaringan Perhatian Grafik Temporal Augmented | Universitas Oxford | AISTAT | 2024 | [PUB] [KODE] |
| Pendakian Penurunan Gradien yang Dihaluskan Stochastic untuk Optimasi Minimax Federasi | Universitas Virginia | AISTAT | 2024 | [PUB] [PDF] |
| Memahami Generalisasi Pembelajaran Federasi melalui Stabilitas: Heterogenitas Itu Penting | Universitas Barat Laut | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Saling Menguntungkan yang Terbukti dari Pembelajaran Federasi dalam Domain yang Sensitif terhadap Privasi | Universitas Sofia | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Analisis Kebocoran Privasi dalam Model Bahasa Besar Federasi | Universitas Florida | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Agregator Invarian untuk Bertahan dari Serangan Pintu Belakang Federasi | UIUC | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| Pembelajaran Federasi yang Efisien Komunikasi Dengan Data dan Heterogenitas Klien | ISTA | AISTAT | 2024 | [PUB] [PDF] [KODE] |
| FedMut: Pembelajaran Federasi Umum melalui Mutasi Stokastik | NTU | AAAI | 2024 | [PUB] |
| Pembelajaran Label Parsial Federasi dengan Augmentasi dan Regularisasi Adaptif Lokal | Universitas Carleton | AAAI | 2024 | [PUB] [HALAMAN] |
| Tidak Ada Prasangka! Jaringan Neural Grafik Federasi yang Adil untuk Rekomendasi yang Dipersonalisasi | IIT | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Logika Formal Memungkinkan Pembelajaran Federasi yang Dipersonalisasi melalui Inferensi Properti | Universitas Vanderbilt | AAAI | 2024 | [PUB] [PDF] |
| Pembelajaran Representasi Pelestarian Privasi Tugas-Agnostik untuk Pembelajaran Federasi terhadap Serangan Inferensi Atribut | Teknologi Illinois | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| FairTrade: Mencapai Pertukaran Pareto-Optimal antara Akurasi yang Seimbang dan Keadilan dalam Pembelajaran Federasi | Universitas Leibniz | AAAI | 2024 | [PUB] [HALAMAN] |
| Memerangi Ketidakseimbangan Data dalam Pembelajaran Semi-supervisi Terfederasi dengan Regulator Ganda | HKUST | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| Fed-QSSL: Kerangka Pembelajaran Federasi yang Dipersonalisasi berdasarkan Bitwidth dan Heterogenitas Data | UT | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| Tentang Penguraian Transfer Pengetahuan Asimetris untuk Pembelajaran Federasi Agnostik Modalitas-Tugas | Universitas Virginia | AAAI | 2024 | [PUB] |
| FedDAT: Pendekatan Penyempurnaan Model Fondasi dalam Pembelajaran Federasi Heterogen Multi-Modal | LMU Munich Siemens AG | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Awasi Kepala Anda: Merakit Kepala Proyeksi untuk Menyelamatkan Keandalan Model Federasi | Laboratorium Kunci Bersama Universitas Xi'an Jiaotong Shaanxi untuk Kecerdasan Buatan | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| FedGCR: Mencapai Kinerja dan Keadilan untuk Pembelajaran Federasi dengan Jenis Klien Berbeda melalui Penyesuaian Grup dan Pembobotan Ulang | NTU | AAAI | 2024 | [PUB] [HALAMAN] [KODE] |
| Encoder Khusus Modalitas Federasi dan Jangkar Multimodal untuk Segmentasi Tumor Otak yang Dipersonalisasi | Universitas Xiamen | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Memanfaatkan Kemiringan Label dalam Pembelajaran Federasi dengan Penggabungan Model | NU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Penyulingan Pengetahuan Pelengkap untuk Pelayanan Model yang Kuat dan Menjaga Privasi dalam Pembelajaran Federasi Vertikal | SUST; HKUST | AAAI | 2024 | [PUB] [HALAMAN] |
| Pembelajaran Federasi melalui Distilasi Kolaboratif Input-Output | Universitas di Buffalo; Sekolah Kedokteran Harvard AS | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Pembelajaran Federasi Satu Putaran yang Dikalibrasi dengan Inferensi Bayesian di Ruang Prediktif | Institut Vektor Universitas Waterloo | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| FedCSL: Pendekatan yang Skalabel dan Akurat untuk Pembelajaran Struktur Kausal Federasi | HFUT | AAAI | 2024 | [PUB] [PDF] |
| FedFixer: Mengurangi Kebisingan Label Heterogen dalam Pembelajaran Federasi | Universitas Xi'an Jiaotong; Universitas Leiden | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| FedLPS: Pembelajaran Federasi Heterogen untuk Banyak Tugas dengan Berbagi Parameter Lokal | NJU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Pembelajaran Trilevel Federasi yang Terbukti Konvergen | TJU | AAAI | 2024 | [PUB] [PDF] |
| Pembelajaran Federasi Performatif: Solusi untuk Pergeseran Distribusi yang Bergantung pada Model dan Heterogen | UM | AAAI | 2024 | [PUB] [HALAMAN] |
| General Commerce Intelligence: Mesin Berbasis NLP yang Terfederasi Secara Global untuk Layanan Multi-Pedagang yang Terpersonalisasi dan Menjaga Privasi secara Berkelanjutan | Universitas Kyung Hee; Harex InfoTech | AAAI | 2024 | [PUB] [HALAMAN] |
| EMGAN: Early-Mix-GAN dalam Mengekstraksi Model Sisi Server dalam Pembelajaran Federasi Terpisah | Sony AI | AAAI | 2024 | [PUB] [HALAMAN] [KODE] |
| FedDiv: Pemfilteran Kebisingan Kolaboratif untuk Pembelajaran Terfederasi dengan Label Bising | SYSU; HKU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Point Transformer dengan Pembelajaran Federasi untuk Memprediksi Status HER2 Kanker Payudara dari Gambar Slide Utuh yang Diwarnai Hematoksilin dan Eosin | USTC; KAS | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| FedNS: Algoritma Tipe Newton Sketsa Cepat untuk Pembelajaran Federasi | KAS | AAAI | 2024 | [PUB] [PDF] [KODE] |
| Bandit Federasi Bersenjata X | Universitas Purdue | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Landasan Algoritma Pembelajaran Federasi dengan Data Berurutan | Universitas Kedokteran Negeri | AAAI | 2024 | [PUB] |
| UFDA: Adaptasi Domain Federasi Universal dengan Asumsi Praktis | XJTU; Universitas Sydney | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| FedASMU: Pembelajaran Federasi Asinkron yang Efisien dengan Pembaruan Model Sadar Staleness Dinamis | Hithink RoyalFlush Information Network Co | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| Transformator Berpanduan Bahasa untuk Klasifikasi Multi-Label Federasi | NTU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| FedCD: Pembelajaran Semi-Supervisi Terfederasi dengan Keseimbangan Kesadaran Kelas melalui Guru Ganda | SZU | AAAI | 2024 | [PUB] [HALAMAN] [KODE] |
| Melampaui Ancaman Tradisional: Serangan Pintu Belakang yang Terus-menerus terhadap Pembelajaran Federasi | HEU | AAAI | 2024 | [PUB] [HALAMAN] [KODE] |
| Pembelajaran Federasi dengan Klien yang Sangat Bising melalui Distilasi Negatif | XMU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| FedST: Pembelajaran Transfer Gaya Federasi untuk Segmentasi Gambar Non-IID | USTB | AAAI | 2024 | [PUB] [HALAMAN] [学报] [KODE] |
| PPIDSG: Skema Berbagi Distribusi Gambar yang Menjaga Privasi dengan GAN dalam Pembelajaran Federasi | USTC | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Kerangka Cognitive Digital Twin (CDT) Berbasis Privacy Preserving Federated Learning (PPFL) untuk Kota Cerdas | DCU | AAAI | 2024 | [PUB] |
| Algoritma Primal-Dual untuk Pembelajaran Federasi Hibrid | Universitas Barat Laut | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| FedLF: Pembelajaran Federasi yang Adil dan Bijaksana | CUHK; Institut Kecerdasan Buatan dan Robotika untuk Masyarakat Shenzhen | AAAI | 2024 | [PUB] [HALAMAN] |
| Menuju Pembelajaran Federasi Grafik yang Adil melalui Mekanisme Insentif | ZJU; FDU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Menuju Kekokohan Pembelajaran Federasi Swasta Diferensial | KAMIS | AAAI | 2024 | [PUB] [HALAMAN] |
| Menolak Serangan Pintu Belakang dalam Pembelajaran Federasi melalui Pemilihan Dua Arah dan Perspektif Individu | ZJU; Huawei | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Integer Sudah Cukup: Saat Pembelajaran Federasi Vertikal Bertemu Pembulatan | ZJU; Grup Semut | AAAI | 2024 | [PUB] [HALAMAN] |
| Pembelajaran Federasi yang Dipandu CLIP tentang Heterogenitas dan Data Berekor Panjang | XMU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Penyesuaian Cepat Adaptif Federasi untuk Pembelajaran Kolaboratif Multi-Domain | FDU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Pembelajaran Federasi Adil Multi-Dimensi | SDU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| HiFi-Gas: Mekanisme Insentif Pembelajaran Federasi Hierarki Meningkatkan Estimasi Penggunaan Gas | Grup ENN | AAAI | 2024 | [PUB] |
| Tentang Peran Momentum Server dalam Pembelajaran Federasi | Universitas Virginia | AAAI | 2024 | [PUB] [PDF] |
| FedCompetitors: Kolaborasi Harmonis dalam Pembelajaran Federasi dengan Peserta yang Bersaing | BUPT | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| z-SignFedAvg: Kompresi Berbasis Tanda Stochastic Terpadu untuk Pembelajaran Federasi | CUHK; Institut Penelitian Big Data China Shenzhen | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| Disparitas Data dan Ketidaktersediaan Temporal Sadar Pembelajaran Federasi Asinkron untuk Pemeliharaan Prediktif pada Armada Transportasi | Grup Volkswagen | AAAI | 2024 | [PUB] [HALAMAN] |
| Pembelajaran Grafik Federasi di bawah Pergeseran Domain dengan Prototipe yang Dapat Digeneralisasikan | WHU | AAAI | 2024 | [PUB] [HALAMAN] |
| TurboSVM-FL: Meningkatkan Pembelajaran Federasi melalui Agregasi SVM untuk Klien Malas | Universitas Teknik Munich | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Minimisasi Perbedaan Gradien Kolaboratif Multi-Sumber untuk Generalisasi Domain Federasi | TJU | AAAI | 2024 | [PUB] [PDF] [KODE] |
| Menyembunyikan Sampel Sensitif terhadap Kebocoran Gradien dalam Pembelajaran Federasi | Universitas Monash | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| FedA3I: Agregasi Sadar Kualitas Anotasi untuk Segmentasi Citra Medis Terfederasi terhadap Kebisingan Anotasi Heterogen | HARUS | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| Pembelajaran Kausalitas Federasi dengan Optimasi Adaptif yang Dapat Dijelaskan | SDU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| Bandit Cascading Kontekstual Federasi dengan Komunikasi Asinkron dan Pengguna Heterogen | USTC | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| Menjelajahi Pembelajaran Federasi Semi-supervisi Satu Pemotretan dengan Model Difusi Terlatih | FDU | AAAI | 2024 | [PUB] [PDF] |
| Stilisasi yang Dibatasi Bersama Keanekaragaman-Keaslian untuk Generalisasi Domain Federasi dalam Identifikasi Ulang Orang | XMU; Universitas Trento | AAAI | 2024 | [PUB] [HALAMAN] |
| PerFedRLNAS: Pencarian Arsitektur Neural Federasi yang Dipersonalisasi dan Satu untuk Semua | kamu dari T | AAAI | 2024 | [PUB] [HALAMAN] |
| Pembelajaran Federasi Asinkron yang Efisien dengan Agregasi Momentum Prospektif dan Koreksi Berbutir Halus | BUPT | AAAI | 2024 | [PUB] [HALAMAN] |
| Serangan Adversarial pada Algoritma Bitrate Adaptif yang Dipelajari Federasi | HKU | AAAI | 2024 | [PUB] |
| FedTGP: Prototipe Global yang Dapat Dilatih dengan Pembelajaran Kontrastif yang Ditingkatkan Margin Adaptif untuk Heterogenitas Data dan Model dalam Pembelajaran Federasi | SJTU | AAAI | 2024 | [PUB] [HALAMAN] [PDF] [KODE] |
| LR-XFL: Pembelajaran Federasi yang Dapat Dijelaskan Berbasis Penalaran Logis | NTU | AAAI | 2024 | [PUB] [PDF] [KODE] |
| Pendekatan Minimalkan Kerugian Huber untuk Pembelajaran Federasi yang Kuat Bizantium | Laboratorium Zhejiang | AAAI | 2024 | [PUB] [HALAMAN] [PDF] |
| Pelatihan Parameter Sadar Pengetahuan untuk Pembelajaran Federasi yang Dipersonalisasi | Universitas Timur Laut | AAAI | 2024 | [PUB] [HALAMAN] |
| Pembelajaran Kebisingan Label Federasi dengan Regularisasi Produk Keanekaragaman Lokal | SJTU | AAAI | 2024 | [PUB] [HALAMAN] [SUPP] |
| Agregasi Tertimbang yang Diadaptasi dalam Pembelajaran Federasi (Abstraksi Siswa) | UBC | AAAI | 2024 | [PUB] |
| Transfer Pengetahuan melalui Model Kompak dalam Pembelajaran Federasi (Abstrak Siswa) | Universitas Sydney | AAAI | 2024 | [PUB] [HALAMAN] |
| PICSR: Router Cross-Silo dengan Informasi Prototipe untuk Pembelajaran Federasi (Abstraksi Siswa) | Lab Auton Universitas Negeri Ohio, CMU | AAAI | 2024 | [PUB] [HALAMAN] |
| Jaringan konvolusi grafik yang menjaga privasi untuk rekomendasi item gabungan | SZU | AI | 2023 | [PUB] |
| Menang-Menang: Kerangka Federasi yang Menjaga Privasi untuk Rekomendasi Lintas-Domain Target Ganda | CAS; UCAS; Teknologi JD; Penelitian Kota Cerdas JD | AAAI | 2023 | [PUB] |
| Serangan Tak Bertarget terhadap Sistem Rekomendasi Federasi melalui Penyematan Barang Beracun dan Pertahanan | USTC; Laboratorium Kunci Negara Kecerdasan Kognitif | AAAI | 2023 | [PUB] [PDF] [KODE] |
| Crowdsourcing Federasi yang Didorong Insentif | SDU | AAAI | 2023 | [PUB] [PDF] |
| Mengatasi Heterogenitas Data dalam Pembelajaran Federasi dengan Prototipe Kelas | Universitas Lehigh | AAAI | 2023 | [PUB] [PDF] [KODE] |
| FairFed: Mengaktifkan Keadilan Kelompok dalam Pembelajaran Federasi | USC | AAAI | 2023 | [PUB] [PDF] [解读] |
| Propagasi Kekokohan Federasi: Berbagi Kekokohan Permusuhan dalam Pembelajaran Federasi Heterogen | Universitas Negeri Moskow | AAAI | 2023 | [PUB] |
| Sparsifikasi Pelengkap: Pemangkasan Model Overhead Rendah untuk Pembelajaran Federasi | NJIT | AAAI | 2023 | [PUB] |
| Komunikasi Hampir Bebas Biaya dalam Identifikasi Lengan Terbaik Federasi | NU | AAAI | 2023 | [PUB] [PDF] |
| Agregasi Model Adaptif Lapisan untuk Pembelajaran Federasi yang Dapat Diskalakan | Universitas Inha Universitas California Selatan | AAAI | 2023 | [PUB] [PDF] |
| Keracunan dengan Cerberus: Serangan Pintu Belakang yang Tersembunyi dan Berkolusi terhadap Pembelajaran Federasi | BJTU | AAAI | 2023 | [PUB] |
| FedMDFG: Pembelajaran Federasi dengan Penurunan Multi-Gradien dan Panduan Adil | CUHK; Institut Kecerdasan Buatan dan Robotika untuk Masyarakat Shenzhen | AAAI | 2023 | [PUB] |
| Mengamankan Agregasi Aman: Mengurangi Kebocoran Privasi Multi-Putaran dalam Pembelajaran Federasi | USC | AAAI | 2023 | [PUB] [PDF] [VIDEO] [KODE] |
| Pembelajaran Gabungan pada Grafik Non-IID melalui Berbagi Pengetahuan Struktural | UTS | AAAI | 2023 | [PUB] [PDF] [KODE] |
| Identifikasi Kesamaan Distribusi yang Efisien dalam Pembelajaran Federasi Terklaster melalui Sudut Utama antara Subruang Data Klien | UCSD | AAAI | 2023 | [PUB] [PDF] [KODE] |
| FedABC: Menargetkan Persaingan Sehat dalam Pembelajaran Federasi yang Dipersonalisasi | WHU; Laboratorium Hubei Luojia; JD Jelajahi Akademi | AAAI | 2023 | [PUB] [PDF] |
| Melampaui ADMM: Kerangka Pembelajaran Federasi Adaptif yang Mengurangi Varians Klien Terpadu | SUTD | AAAI | 2023 | [PUB] [PDF] |
| FedGS: Pengambilan Sampel Berbasis Grafik Federasi dengan Ketersediaan Klien Sewenang-wenang | XMU | AAAI | 2023 | [PUB] [PDF] [KODE] |
| Pembelajaran Federasi Adaptif yang Lebih Cepat | Universitas Pittsburg | AAAI | 2023 | [PUB] [PDF] |
| FedNP: Menuju Pembelajaran Federasi Non-IID melalui Propagasi Neural Federasi | HKUST | AAAI | 2023 | [PUB] [KODE] [VIDEO] [SUPP] |
| Pencocokan Neural Federasi Bayesian yang Melengkapi Informasi Lengkap | TJU | AAAI | 2023 | [PUB] [PDF] |
| CDMA: Algoritma Pembelajaran Federasi Lintas Perangkat Praktis untuk Masalah Umum Minimax | ZJU | AAAI | 2023 | [PUB] [PDF] [KODE] |
| Model Generatif Federasi pada Data Heterogen Multi-Sumber di IoT | GSU | AAAI | 2023 | [PUB] |
| DeFL: Bertahan dari Serangan Keracunan Model dalam Pembelajaran Federasi melalui Kesadaran Periode Pembelajaran Kritis | Universitas SUNY-Binghamton | AAAI | 2023 | [PUB] |
| FedALA: Agregasi Lokal Adaptif untuk Pembelajaran Federasi yang Dipersonalisasi | SJTU | AAAI | 2023 | [PUB] [PDF] [KODE] |
| Menggali Kekokohan Pembelajaran Federasi | ZJU | AAAI | 2023 | [PUB] [PDF] |
| Tentang Kerentanan Pertahanan Pintu Belakang untuk Pembelajaran Federasi | TJU | AAAI | 2023 | [PUB] [PDF] [KODE] |
| Gema Tetangga: Penguatan Privasi untuk Pembelajaran Federasi Swasta yang Dipersonalisasi dengan Model Acak | RUC; Pusat Penelitian Teknik Kemendikbud tentang Database dan BI | AAAI | 2023 | [PUB] [PDF] |
| DPAUC: Komputasi AUC Privat Diferensial dalam Pembelajaran Federasi | ByteDance Inc. | Lagu Khusus AAAI | 2023 | [PUB] [PDF] [KODE] |
| Pelatihan Efisien Model Diagnostik Kesalahan Industri Skala Besar melalui Federated Opportunistic Block Dropout | NTU | Program Khusus AAAI | 2023 | [PUB] [PDF] |
| Pembelajaran Federasi yang Diatur Skala Industri untuk Penemuan Obat | KU Leuven | Program Khusus AAAI | 2023 | [PUB] [PDF] [VIDEO] |
| Alat Monitoring Pembelajaran Federasi untuk Simulasi Mobil Self-Driving (Abstrak Siswa) | CNU | Program Khusus AAAI | 2023 | [PUB] |
| MGIA: Serangan Inversi Gradien Timbal Balik dalam Pembelajaran Federasi Multi-Modal (Abstrak Siswa) | PoliU | Program Khusus AAAI | 2023 | [PUB] |
| Pembelajaran Federasi Terklaster untuk Data Heterogen (Abstraksi Siswa) | RUC | Program Khusus AAAI | 2023 | [PUB] |
| FedSampling: Strategi Sampling yang Lebih Baik untuk Pembelajaran Federasi | KAMIS | IJCAI | 2023 | [PUB] [PDF] [KODE] |
| HyperFed: Eksplorasi Prototipe Hiperbolik dengan Agregasi yang Konsisten untuk Data Non-IID dalam Pembelajaran Federasi | ZJU | IJCAI | 2023 | [PUB] [PDF] |
| FedOBD: Dropout Blok Oportunistik untuk Melatih Jaringan Neural Skala Besar secara Efisien melalui Pembelajaran Federasi | NTU | IJCAI | 2023 | [PUB] [PDF] [KODE] |
| Pemodelan Distribusi Preferensi Probabilistik Federasi dengan Co-Clustering yang Kompak untuk Rekomendasi Multi-Domain yang Menjaga Privasi | ZJU | IJCAI | 2023 | [PUB] |
| Pembelajaran Semantik dan Struktural Grafik Gabungan | WHU | IJCAI | 2023 | [PUB] |
| BARA: Mekanisme Insentif yang Efisien dengan Alokasi Anggaran Reward Online dalam Pembelajaran Federasi Lintas-Silo | SYSU | IJCAI | 2023 | [PUB] [PDF] |
| FedDWA: Pembelajaran Federasi yang Dipersonalisasi dengan Penyesuaian Berat Dinamis | SYSU | IJCAI | 2023 | [PUB] [PDF] |
| FedPass: Pembelajaran Mendalam Federasi Vertikal yang Menjaga Privasi dengan Kebingungan Adaptif | bank web | IJCAI | 2023 | [PUB] [PDF] |
| Autoencoder Grafik Federasi yang Konsisten Secara Global untuk Grafik Non-IID | FZU | IJCAI | 2023 | [PUB] [KODE] |
| Pembelajaran Penguatan Multi-Agen Kooperatif-Kompetitif untuk Pembelajaran Federasi Berbasis Lelang | NTU | IJCAI | 2023 | [PUB] |
| Personalisasi Ganda pada Rekomendasi Federasi | JLU; Universitas Teknologi Sydney | IJCAI | 2023 | [PUB] [PDF] [KODE] |
| FedNoRo: Menuju Pembelajaran Federasi yang Kuat dengan Kebisingan dengan Mengatasi Ketidakseimbangan Kelas dan Label Heterogenitas Kebisingan | HARUS | IJCAI | 2023 | [PUB] [PDF] [KODE] |
| Penolakan Layanan atau Kontrol Halus: Menuju Serangan Keracunan Model Fleksibel pada Pembelajaran Federasi | Universitas Xiangtan | IJCAI | 2023 | [PUB] [PDF] [KODE] |
| FedHGN: Kerangka Kerja Federasi untuk Jaringan Neural Grafik Heterogen | CUHK | IJCAI | 2023 | [PUB] [PDF] [KODE] |
| FedET: Kerangka Pembelajaran Inkremental Kelas Federasi yang Efisien Komunikasi Berdasarkan Transformator yang Ditingkatkan | Teknologi Ping An; KAMIS | IJCAI | 2023 | [PUB] [PDF] |
| Pembelajaran Federasi yang Cepat untuk Prakiraan Cuaca: Menuju Model Dasar Data Meteorologi | UTS | IJCAI | 2023 | [PUB] [PDF] [KODE] |
| FedBFPT: Kerangka Pembelajaran Federasi yang Efisien untuk Pra-pelatihan Lebih Lanjut Bert | ZJU | IJCAI | 2023 | [PUB] [KODE] |
| Pembelajaran Federasi Bayesian: Sebuah Survei | Jalur Survei IJCAI | 2023 | [PDF] | |
| Survei Evaluasi Federasi dalam Pembelajaran Federasi | Universitas Macquarie | Jalur Survei IJCAI | 2023 | [PUB] [PDF] |
| SAMBA: Kerangka Umum untuk Federasi Bandit Multi-Bersenjata yang Aman (Abstrak Diperpanjang) | Pusat INSA Val de Loire | Jalur Jurnal IJCAI | 2023 | [PUB] |
| Biaya komunikasi keamanan dan privasi dalam estimasi frekuensi gabungan | Stanford | AISTAT | 2023 | [PUB] [KODE] |
| Pembelajaran Federasi yang Efisien dan Ringan melalui Dropout Terdistribusi Asinkron | Universitas Beras | AISTAT | 2023 | [PUB] [KODE] |
| Pembelajaran Federasi di bawah Konsep Drift Terdistribusi | CMU | AISTAT | 2023 | [PUB] [KODE] |
| Mengkarakterisasi Serangan Penghindaran Internal dalam Pembelajaran Federasi | CMU | AISTAT | 2023 | [PUB] [KODE] |
| Asimptotik Federasi: model untuk membandingkan algoritma pembelajaran gabungan | Stanford | AISTAT | 2023 | [PUB] [KODE] |
| Pembelajaran Federasi Non-Cembung Pribadi Tanpa Server Tepercaya | USC | AISTAT | 2023 | [PUB] [KODE] |
| Pembelajaran Federasi untuk Aliran Data | Universitas ́ e Cˆ ote d'Azur | AISTAT | 2023 | [PUB] [KODE] |
| Hanya Penyesalan — Penemuan Kausal Federasi yang Menjaga Privasi | Pusat Keamanan Informasi Helmholtz | AISTAT | 2023 | [PUB] [KODE] |
| Serangan Inferensi Keanggotaan Aktif di bawah Privasi Diferensial Lokal dalam Pembelajaran Federasi | UFL | AISTAT | 2023 | [PUB] [KODE] |
| Dinamika Langevin Rata-rata Federasi: Menuju teori terpadu dan algoritma baru | CMAP | AISTAT | 2023 | [PUB] |
| Pembelajaran Federasi yang Kuat Bizantium dengan Tingkat Statistik Optimal | UC Berkeley | AISTAT | 2023 | [PUB] [KODE] |
| Pembelajaran Gabungan pada Grafik Non-IID melalui Berbagi Pengetahuan Struktural | UTS | AAAI | 2023 | [PDF] [KODE] |
| FedGS: Pengambilan Sampel Berbasis Grafik Federasi dengan Ketersediaan Klien Sewenang-wenang | XMU | AAAI | 2023 | [PDF] [KODE] |
| Crowdsourcing Federasi yang didorong oleh insentif | SDU | AAAI | 2023 | [PDF] |
| Menuju Memahami Seleksi Klien yang Bias dalam Pembelajaran Federasi. | CMU | AISTAT | 2022 | [PUB] [KODE] |
| FLIX: Alternatif Sederhana dan Efisien Komunikasi terhadap Metode Lokal dalam Pembelajaran Federasi | KAUST | AISTAT | 2022 | [PUB] [PDF] [KODE] |
| Batasan Tajam untuk Rata-Rata Federasi (SGD Lokal) dan Perspektif Kontinu. | Stanford | AISTAT | 2022 | [PUB] [PDF] [KODE] |
| Pembelajaran Penguatan Federasi dengan Heterogenitas Lingkungan. | PKU | AISTAT | 2022 | [PUB] [PDF] [KODE] |
| Deteksi Komunitas Miopia Federasi dengan Komunikasi Sekali Pakai | Purdue | AISTAT | 2022 | [PUB] [PDF] |
| Algoritma terikat kepercayaan atas asinkron untuk bandit linier federasi. | Universitas Virginia | Aistats | 2022 | [Pub] [PDF] [Kode] |
| Menuju Pembelajaran Struktur Jaringan Bayesian Federated dengan optimasi berkelanjutan. | CMU | Aistats | 2022 | [Pub] [PDF] [Kode] |
| Pembelajaran federasi dengan agregasi asinkron buffered | Meta AI | Aistats | 2022 | [Pub] [PDF] [Video] |
| Pembelajaran federasi pribadi yang berbeda pada data heterogen. | Stanford | Aistats | 2022 | [Pub] [PDF] [Kode] |
| Sparsefed: Mitigating Model Racun Serangan dalam Pembelajaran Federasi dengan Sparsifikasi | Princeton | Aistats | 2022 | [Pub] [PDF] [kode] [Video] |
| Basis penting: metode urutan kedua yang lebih efisien komunikasi yang lebih baik untuk pembelajaran federasi | Kaust | Aistats | 2022 | [Pub] [PDF] |
| Peningkatan gradien fungsional federasi. | Universitas Pennsylvania | Aistats | 2022 | [Pub] [PDF] [Kode] |
| QLSD: Dinamika stokastik Langevin yang diukur untuk pembelajaran federasi Bayesian. | Lab Criteo Ai | Aistats | 2022 | [Pub] [PDF] [kode] [Video] |
Ekstrapolasi pengetahuan berbasis meta-pembelajaran untuk grafik pengetahuan dalam pengaturan federasi kg. | Zju | Ijcai | 2022 | [Pub] [PDF] [Kode] |
| Pembelajaran federasi yang dipersonalisasi dengan grafik | UTS | Ijcai | 2022 | [Pub] [PDF] [Kode] |
| Jaringan saraf grafik federasi vertikal untuk klasifikasi simpul pemeliharaan privasi | Zju | Ijcai | 2022 | [Pub] [PDF] |
| Beradaptasi dengan adaptasi: belajar personalisasi untuk pembelajaran federasi silang-silo | Ijcai | 2022 | [Pub] [PDF] [Kode] | |
| Transfer Pengetahuan Ensemble Heterogen untuk Melatih Model Besar dalam Pembelajaran Federasi | Ijcai | 2022 | [Pub] [PDF] | |
| Pembelajaran federasi semi-diawasi pribadi. | Ijcai | 2022 | [Pub] | |
| Pembelajaran federasi yang berkelanjutan berdasarkan distilasi pengetahuan. | Ijcai | 2022 | [Pub] | |
| Pembelajaran federasi pada data yang heterogen dan berekor panjang melalui pelatihan ulang classifier dengan fitur federasi | Ijcai | 2022 | [Pub] [PDF] [Kode] | |
| Perhatian multi-tugas federasi untuk pengakuan aktivitas manusia lintas-individu | Ijcai | 2022 | [Pub] | |
| Pembelajaran federasi yang dipersonalisasi dengan generalisasi kontekstual. | Ijcai | 2022 | [Pub] [PDF] | |
| Shielding Federated Learning: Agregasi yang kuat dengan pemilihan klien adaptif. | Ijcai | 2022 | [Pub] [PDF] | |
| FedCG: Leverage GAN bersyarat untuk melindungi privasi dan mempertahankan kinerja kompetitif dalam pembelajaran federasi | Ijcai | 2022 | [Pub] [PDF] [Kode] | |
| FedDuap: Pembelajaran Federasi dengan Pembaruan Dinamis dan Pemangkasan Adaptif Menggunakan Data Bersama di Server. | Ijcai | 2022 | [Pub] [PDF] | |
Menuju pembelajaran federasi yang dapat diverifikasi surv. | Ijcai | 2022 | [Pub] [PDF] | |
| Harmofl: Harmonisasi penyimpangan lokal dan global dalam pembelajaran federasi pada gambar medis yang heterogen | Cuhk; Buaa | Aaai | 2022 | [Pub] [PDF] [kode] [解读] |
| Pembelajaran federasi untuk pengenalan wajah dengan koreksi gradien | BUTT | Aaai | 2022 | [Pub] [PDF] |
| Spreadgnn: Pembelajaran federasi multi-tugas terdesentralisasi untuk grafik jaringan saraf pada data molekuler | USC | Aaai | 2022 | [Pub] [PDF] [kode] [解读] |
| SmartIdx: Mengurangi biaya komunikasi dalam pembelajaran federasi dengan mengeksploitasi struktur CNNS | MEMUKUL; Pcl | Aaai | 2022 | [Pub] [kode] |
| Menjembatani antara sinyal pemrosesan kognitif dan fitur linguistik melalui jaringan perhatian terpadu | Tju | Aaai | 2022 | [Pub] [PDF] |
| Merebut periode pembelajaran kritis dalam pembelajaran federasi | Universitas SUNY-BINGHAMTON | Aaai | 2022 | [Pub] [PDF] |
| Koordinasi momen untuk pembelajaran federasi silang-silo | Universitas Pittsburgh | Aaai | 2022 | [Pub] [PDF] |
| FedProto: Pembelajaran prototipe federasi melalui perangkat yang heterogen | UTS | Aaai | 2022 | [Pub] [PDF] [Kode] |
| FedSoft: Pembelajaran federasi yang dikelompokkan dengan lembut dengan pembaruan lokal proksimal | CMU | Aaai | 2022 | [Pub] [PDF] [Kode] |
| Pelatihan Jarang Dinamis Federasi: Komputasi Lebih sedikit, berkomunikasi lebih sedikit, namun belajar lebih baik | Universitas Texas di Austin | Aaai | 2022 | [Pub] [PDF] [Kode] |
| Fedfr: Optimasi bersama Federated Framework untuk pengakuan wajah generik dan personalisasi | Universitas Taiwan Nasional | Aaai | 2022 | [Pub] [PDF] [Kode] |
| SplitFed: Ketika Pembelajaran Federasi Bertemu Pembelajaran Berpisah | Csiro | Aaai | 2022 | [Pub] [PDF] [Kode] |
| Penjadwalan perangkat yang efisien dengan pembelajaran federasi multi-pekerjaan | Universitas Soochow | Aaai | 2022 | [Pub] [PDF] |
| Penyelarasan gradien implisit dalam pembelajaran terdistribusi dan federasi | IIT Kanpur | Aaai | 2022 | [Pub] [PDF] |
| Klasifikasi tetangga terdekat yang digemorasikan dengan koloni buah-buahan | Penelitian IBM | Aaai | 2022 | [Pub] [PDF] [Kode] |
| Bidang vektor dan konservatisme iterasi, dengan aplikasi untuk pembelajaran federasi. | ALT | 2022 | [Pub] [PDF] | |
| Pembelajaran federasi dengan privasi yang diamplifikasi sparsifikasi dan optimasi adaptif | Ijcai | 2021 | [Pub] [PDF] [Video] | |
| Perilaku meniru distribusi: Menggabungkan perilaku individu dan kelompok untuk pembelajaran federasi | Ijcai | 2021 | [Pub] [PDF] | |
| FedSpeech: Teks-ke-Speech Federasi dengan Pembelajaran Berkelanjutan | Ijcai | 2021 | [Pub] [PDF] | |
| Pembelajaran federasi satu-shot praktis untuk pengaturan silang silo | Ijcai | 2021 | [Pub] [PDF] [Kode] | |
| Distilasi model federasi dengan privasi diferensial bebas noise | Ijcai | 2021 | [Pub] [PDF] [Video] | |
| LDP-FL: Agregasi pribadi praktis dalam pembelajaran federasi dengan privasi diferensial lokal | Ijcai | 2021 | [Pub] [PDF] | |
| Pembelajaran federasi dengan rata -rata yang adil. | Ijcai | 2021 | [Pub] [PDF] [Kode] | |
| H-FL: Arsitektur hirarki-efisien dan privasi yang dilindungi privasi untuk pembelajaran federasi. | Ijcai | 2021 | [Pub] [PDF] | |
| Pembelajaran tepi federasi yang efisien dan terukur komunikasi. | Ijcai | 2021 | [Pub] | |
| Amankan pembelajaran federasi vertikal bilevel asinkron dengan pembaruan mundur | Universitas Xidian; JD Tech | Aaai | 2021 | [Pub] [PDF] [Video] |
| FedRec ++: Rekomendasi federasi yang lossless dengan umpan balik eksplisit | Szu | Aaai | 2021 | [Pub] [Video] |
| Bandit multi-bersenjata federasi | Universitas Virginia | Aaai | 2021 | [Pub] [PDF] [kode] [Video] |
| Tentang konvergensi SGD lokal yang hemat komunikasi untuk pembelajaran federasi | Universitas Kuil; Universitas Pittsburgh | Aaai | 2021 | [Pub] [Video] |
| Api: Pembelajaran federasi pribadi yang berbeda dalam model shuffle | Renmin University of China; Universitas Kyoto | Aaai | 2021 | [Pub] [PDF] [Video] [Kode] |
| Untuk memahami pengaruh klien individu dalam pembelajaran federasi | Sjtu; Universitas Texas di Dallas | Aaai | 2021 | [Pub] [PDF] [Video] |
| Terbukti mendapatkan pembelajaran federasi terhadap klien jahat | Universitas Duke | Aaai | 2021 | [Pub] [PDF] [Video] [Slide] |
| Pembelajaran federasi silang-silo yang dipersonalisasi pada data non-IID | Universitas Simon Fraser; Universitas McMaster | Aaai | 2021 | [Pub] [PDF] [Video] [UC.] |
| Model-Sharing Games: Menganalisis Pembelajaran Federasi di bawah Partisipasi Sukarela | Universitas Cornell | Aaai | 2021 | [Pub] [PDF] [kode] [Video] |
| Kutukan atau penebusan? Bagaimana Heterogenitas Data Mempengaruhi Ketahanan Pembelajaran Federasi | Universitas Nevada; Penelitian IBM | Aaai | 2021 | [Pub] [PDF] [Video] |
| Game Of Gradients: Mengurangi klien yang tidak relevan dalam pembelajaran federasi | IIT Bombay; Penelitian IBM | Aaai | 2021 | [Pub] [PDF] [kode] [Video] [Supp] |
| Skema Keturunan Koordinat Blok Federasi untuk Mempelajari Model Global dan Personalisasi | Cuhk; Arizona State University | Aaai | 2021 | [Pub] [PDF] [Video] [Kode] |
| Mengatasi ketidakseimbangan kelas dalam pembelajaran federasi | Universitas Barat Laut | Aaai | 2021 | [Pub] [PDF] [Video] [kode] [解读] |
| Bertahan melawan pintu belakang dalam pembelajaran federasi dengan tingkat pembelajaran yang kuat | Universitas Texas di Dallas | Aaai | 2021 | [Pub] [PDF] [Video] [Kode] |
| Serangan pengendara bebas pada agregasi model dalam pembelajaran federasi | Accenture Labs | Aistats | 2021 | [Pub] [PDF] [kode] [Video] [Supp] |
| Privasi F gabungan Federated | Universitas Pennsylvania | Aistats | 2021 | [Pub] [kode] [video] [Supp] |
| Pembelajaran federasi dengan kompresi: analisis terpadu dan jaminan tajam | Universitas Negeri Pennsylvania; Universitas Texas di Austin | Aistats | 2021 | [Pub] [PDF] [kode] [Video] [Supp] |
| Model privasi diferensial yang dikocok dalam pembelajaran federasi | UCLA; Google | Aistats | 2021 | [Pub] [Video] [Supp] |
| Pertukaran konvergensi dan akurasi dalam pembelajaran dan pembelajaran meta | Aistats | 2021 | [Pub] [PDF] [Video] [Supp] | |
| Bandit multi-bersenjata federasi dengan personalisasi | Universitas Virginia; Universitas Negeri Pennsylvania | Aistats | 2021 | [Pub] [PDF] [kode] [Video] [Supp] |
| Menuju partisipasi perangkat yang fleksibel dalam pembelajaran federasi | CMU; Sysu | Aistats | 2021 | [Pub] [PDF] [Video] [Supp] |
| Pembelajaran meta federasi untuk deteksi kartu kredit yang curang | Ijcai | 2020 | [Pub] [Video] | |
| Game multi-pemain untuk mempelajari skema insentif pembelajaran federasi | Ijcai | 2020 | [Pub] [kode] [解读] | |
| Praktis Federated Gradient Meningkatkan Pohon Keputusan | Nus; Uwa | Aaai | 2020 | [Pub] [PDF] [Kode] |
| Pembelajaran federasi untuk masalah pentanahan penglihatan dan bahasa | PKU; Tencent | Aaai | 2020 | [Pub] |
| Alokasi Dirichlet Laten Federasi: Kerangka Berbasis Privasi Diferensial Lokal | Buaa | Aaai | 2020 | [Pub] |
| Hashing pasien federasi | Universitas Cornell | Aaai | 2020 | [Pub] |
| Pembelajaran federasi yang kuat melalui pengajaran mesin kolaboratif | Symantec Research Labs; Kaust | Aaai | 2020 | [Pub] [PDF] |
| FedVision: Platform Deteksi Objek Visual Online Didukung oleh Pembelajaran Federasi | Webank | Aaai | 2020 | [Pub] [PDF] [Kode] |
| FedPAQ: Metode pembelajaran federasi yang hemat komunikasi dengan rata-rata dan kuantisasi berkala | UC Santa Barbara; Ut austin | Aistats | 2020 | [Pub] [PDF] [Video] [Supp] |
| Cara Pembelajaran Federasi Pintu Belakang | Cornell Tech | Aistats | 2020 | [Pub] [PDF] [Video] [kode] [Supp] |
| Penemuan pemukul berat federasi dengan privasi diferensial | RPI; Google | Aistats | 2020 | [Pub] [PDF] [Video] [Supp] |
| Visualisasi multi-agen untuk menjelaskan pembelajaran federasi | Webank | Ijcai | 2019 | [Pub] [Video] |
Makalah Pembelajaran Federasi Diterima oleh Konferensi dan Jurnal dan Jurnal ML Teratas, termasuk Neurips (Konferensi Tahunan tentang Sistem Pemrosesan Informasi Saraf), ICML (Konferensi Internasional tentang Pembelajaran Mesin), ICLR (Konferensi Internasional tentang Representasi Pembelajaran), COLT (Konferensi Konferensi Tahunan Konferensi Tahunan) Teori Pembelajaran), UAI (Konferensi Ketidakpastian dalam Kecerdasan Buatan), Pembelajaran Mesin, JMLR (Jurnal Penelitian Pembelajaran Mesin), TPAMI (Transaksi IEEE pada Analisis Pola dan kecerdasan mesin).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| Menstabilkan dan mempercepat pembelajaran federasi pada data heterogen dengan partisipasi klien parsial | Tpami | 2025 | [Pub] | |
| Model federasi medis dengan campuran komponen yang dipersonalisasi dan dibagikan | Tpami | 2025 | [Pub] | |
| Pembelajaran federasi satu-shot melalui komunikasi distiller sintetis | Neurips | 2024 | [Pub] | |
| Pembelajaran federasi nonkonveks pada submanifold yang halus dengan data heterogen | Neurips | 2024 | [Pub] | |
| FedGMKD: Kerangka belajar federasi prototipe yang efisien melalui distilasi pengetahuan dan agregasi sadar-perbedaan | Neurips | 2024 | [Pub] | |
| Meningkatkan Generalisasi dalam Pembelajaran Federasi dengan Model-Data Mutual Information Regelegurikanisasi: Pendekatan Inferensi Posterior | Neurips | 2024 | [Pub] | |
| Model Federated Heterogen Matryoshka Representation Learning | Neurips | 2024 | [Pub] | |
| Pembelajaran Grafik Federasi untuk Rekomendasi Cross-Domain | Neurips | 2024 | [Pub] | |
| FedGmark: Watermarking yang kuat dan kuat untuk pembelajaran grafik federasi | Neurips | 2024 | [Pub] | |
| Adaptor Dual-Personalisasi untuk Model Yayasan Federasi | Neurips | 2024 | [Pub] | |
| Gradien Kebijakan Alam Federasi dan Metode Kritik Aktor untuk Pembelajaran Penguatan Multi-Task | Neurips | 2024 | [Pub] | |
| Menjinakkan ekor panjang dalam prediksi mobilitas manusia | Neurips | 2024 | [Pub] | |
| Dual Defense: Meningkatkan privasi dan meringankan serangan keracunan dalam pembelajaran federasi | Neurips | 2024 | [Pub] | |
| Optimizer yang ditingkatkan grafik untuk Evolution Embedding Rekomendasi Struktur-Struktur | Neurips | 2024 | [Pub] | |
| DOFIT: Tuning instruksi federasi domain dengan lupa katastropik yang dikurangi | Neurips | 2024 | [Pub] | |
| Pembelajaran federasi yang efisien terhadap tidak tersedianya klien yang heterogen dan non-stasioner | Neurips | 2024 | [Pub] | |
| Federated Transformer: Multi-Party Vertikal Pembelajaran Federated pada Data Terkait Praktis Fuzzily | Neurips | 2024 | [Pub] | |
| Fiarse: Pembelajaran federasi model-heterogen melalui ekstraksi submodel yang tidak sadar penting | Neurips | 2024 | [Pub] | |
| Prompt tuning federasi probabilistik dengan data non-IID dan tidak seimbang | Neurips | 2024 | [Pub] | |
| Flora: Model bahasa besar yang menyempurnakan federasi dengan adaptasi rendah yang heterogen | Neurips | 2024 | [Pub] | |
| Menjinakkan varian representasi lintas domain dalam pembelajaran prototipe federasi dengan domain data yang heterogen | Neurips | 2024 | [Pub] | |
| PfedClub: Agregasi model heterogen yang dapat dikendalikan untuk pembelajaran federasi yang dipersonalisasi | Neurips | 2024 | [Pub] | |
| Mengapa Purnama? Meningkatkan pembelajaran federasi melalui pembaruan jaringan parsial | Neurips | 2024 | [Pub] | |
| FUSEFL: Pembelajaran federasi satu-shot melalui lensa kausalitas dengan fusi model progresif | Neurips | 2024 | [Pub] | |
| FedSSP: Pembelajaran grafik federasi dengan pengetahuan spektral dan preferensi yang dipersonalisasi | Neurips | 2024 | [Pub] | |
| Menangani Perusahaan Belajar Dari Ruang Fitur Heterogen dengan Eksploitasi Label Eksplisit | Neurips | 2024 | [Pub] | |
| A-FEDPD: Menyelaraskan Dual-Drift adalah semua kebutuhan belajar dual primal federasi | Neurips | 2024 | [Pub] | |
| Estimasi frekuensi pribadi dan personal dalam pengaturan federasi | Neurips | 2024 | [Pub] | |
| Trade-off kompleksitas komunikasi sampel dalam q-learning federasi | Neurips | 2024 | [Pub] | |
| Pembelajaran penguatan offline yang diarahkan oleh ansambel federasi | Neurips | 2024 | [Pub] | |
| Adaptasi kotak hitam federasi untuk segmentasi semantik | Neurips | 2024 | [Pub] | |
| Berpikir ke depan: Finetuning finetuning finetuning dari model bahasa yang hemat memori | Neurips | 2024 | [Pub] | |
| Pembelajaran Federasi dari Model Yayasan Visi-Bahasa: Analisis dan Metode Teoritis | Neurips | 2024 | [Pub] | |
| Desain optimal untuk elisitasi preferensi manusia | Neurips | 2024 | [Pub] | |
| Menuju berbagai perangkat pembelajaran federasi heterogen melalui integrasi pengetahuan aritmatika tugas | Neurips | 2024 | [Pub] | |
| Pembelajaran federasi yang dipersonalisasi melalui adaptasi distribusi fitur | Neurips | 2024 | [Pub] | |
| SCAFFLSA: menjinakkan heterogenitas dalam perkiraan stokastik linier federasi dan pembelajaran TD | Neurips | 2024 | [Pub] | |
| Pendekatan Bayesian untuk Pembelajaran Federasi yang Dipersonalisasi di Pengaturan Heterogen | Neurips | 2024 | [Pub] | |
| RFLPA: Kerangka pembelajaran yang kuat, melawan serangan keracunan dengan agregasi yang aman | Neurips | 2024 | [Pub] | |
| FedGTST: Meningkatkan transferabilitas global model federasi melalui penyetelan statistik | Neurips | 2024 | [Pub] | |
| Clustering yang dapat dipelajari dari ujung ke ujung untuk pembelajaran niat dalam rekomendasi | Neurips | 2024 | [Pub] | |
| Fedlpa: Pembelajaran federasi satu-shot dengan agregasi posterior lapisan bijaksana | Neurips | 2024 | [Pub] | |
| Waktu-FFM: Menuju Model Federated Founderated Foundation LM untuk peramalan deret waktu | Neurips | 2024 | [Pub] | |
| FOOGD: Kolaborasi federasi untuk generalisasi dan deteksi di luar distribusi dan deteksi | Neurips | 2024 | [Pub] | |
| Pisau Tentara Swiss untuk Pembelajaran Federasi yang heterogen: Kopling fleksibel melalui norma jejak | Neurips | 2024 | [Pub] | |
| Fedne: Tetangga federasi yang dibantu pengganti untuk pengurangan dimensionalitas | Neurips | 2024 | [Pub] | |
| Pelatihan lokal presisi rendah sudah cukup untuk pembelajaran federasi | Neurips | 2024 | [Pub] | |
| Pembelajaran diri sendiri yang sadar akan sumber daya dengan representasi kelas global | Neurips | 2024 | [Pub] | |
| Tentang kebutuhan kolaborasi untuk pemilihan model online dengan data terdesentralisasi | Neurips | 2024 | [Pub] | |
| Kekuatan ekstrapolasi dalam pembelajaran federasi | Neurips | 2024 | [Pub] | |
| (Fl) $^2 $: Mengatasi beberapa label dalam pembelajaran semi-diawasi federasi | Neurips | 2024 | [Pub] | |
| Pada strategi pengambilan sampel untuk sharding model spektral | Neurips | 2024 | [Pub] | |
| Menyesuaikan Model Bahasa dengan Lora yang bijaksana untuk rekomendasi berurutan | Neurips | 2024 | [Pub] | |
| SPAFL: Pembelajaran federasi yang hemat komunikasi dengan model yang jarang dan overhead komputasi rendah | Neurips | 2024 | [Pub] | |
| Hydra-Fl: Distilasi Pengetahuan Hibrida untuk Pembelajaran Federasi yang Kuat dan Akurat | Neurips | 2024 | [Pub] | |
| Metode titik proksimal yang stabil untuk optimasi federasi | Neurips | 2024 | [Pub] | |
| Dapperfl: Domain Adaptive Federated Learning dengan Model Fusion Pruning untuk Perangkat Edge | Neurips | 2024 | [Pub] | |
| Parameter diseksi diseksi untuk pertahanan pintu belakang dalam pembelajaran federasi yang heterogen | Neurips | 2024 | [Pub] | |
| Apakah agen berkinerja terburuk memimpin paket? Menganalisis dinamika agen dalam SGD terdistribusi terpadu | Neurips | 2024 | [Pub] | |
| FedAVP: Menambah data lokal melalui kebijakan bersama dalam pembelajaran federasi | Neurips | 2024 | [Pub] | |
| Cobo: Pembelajaran Kolaboratif melalui Optimalisasi Bilevel | Neurips | 2024 | [Pub] | |
| Analisis konvergensi pembelajaran federasi terpisah pada data heterogen | Neurips | 2024 | [Pub] | |
| Kelompok federasi yang efisien komunikasi optimasi yang kuat secara distribusi | Neurips | 2024 | [Pub] | |
| Ferrari: Fitur Federated Menggunakan melalui Mengoptimalkan Sensitivitas Fitur | Neurips | 2024 | [Pub] | |
| Pembelajaran federasi melalui mode yang terhubung | Neurips | 2024 | [Pub] | |
| Pembelajaran federasi yang dipersonalisasi dengan campuran model untuk prediksi adaptif dan fine-tuning model | Neurips | 2024 | [Pub] | |
| Apakah keadilan egaliter menyebabkan ketidakstabilan? Batas keadilan dalam pembelajaran federasi yang stabil di bawah perilaku altruistik | Neurips | 2024 | [Pub] | |
| Prediksi online federasi dari para ahli dengan privasi diferensial: pemisahan dan penyesalan speed-up | Neurips | 2024 | [Pub] | |
| DataStealing: mencuri data dari model difusi dalam pembelajaran federasi dengan banyak trojan | Neurips | 2024 | [Pub] | |
| Pesawat Perilaku Federasi: Menjelaskan Evolusi Perilaku Klien dalam Pembelajaran Federasi | Neurips | 2024 | [Pub] | |
| Pembelajaran federasi hierarkis dengan koreksi gradien multi-waktu | Neurips | 2024 | [Pub] | |
| Hiperprism: Kerangka kerja agregasi non-linear adaptif untuk pembelajaran mesin terdistribusi melalui data non-IID dan tautan komunikasi yang bervariasi waktu | Neurips | 2024 | [Pub] | |
| Tombak: inversi gradien yang tepat dari batch dalam pembelajaran federasi | Neurips | 2024 | [Pub] | |
| Pembelajaran federasi di bawah partisipasi klien berkala dan data heterogen: algoritma dan analisis yang efisien komunikasi baru | Neurips | 2024 | [Pub] | |
| Bridging Gaps: Federated Multi-View Clustering dalam tampilan hibrida heterogen | Neurips | 2024 | [Pub] | |
| Pembelajaran federasi yang tahan kebingungan melalui harmonisasi data berbasis difusi pada data non-IID | Neurips | 2024 | [Pub] | |
| Sup Superior Lokal: Katalis untuk Penggabungan Model dalam Pembelajaran Federasi Silang-Silo | Neurips | 2024 | [Pub] | |
| Pembentukan Kolaborasi Pengendara Bebas dan Konflik Konflik untuk Pembelajaran Federasi Silang-Silo | Neurips | 2024 | [Pub] | |
| Classifier clustering dan fitur penyelarasan untuk pembelajaran federasi di bawah drift konsep terdistribusi | Neurips | 2024 | [Pub] | |
| Pengambilan Sampel Klien yang Dipandu Heterogenitas: Menuju Pembelajaran Federasi Non-IID yang Cepat dan Efisien | Neurips | 2024 | [Pub] | |
| Fakta atau Fiksi: Dapatkah mekanisme yang jujur menghilangkan berkuda bebas federasi? | Neurips | 2024 | [Pub] | |
| Pembelajaran preferensi aktif untuk memesan item di dalam dan di luar sampel | Neurips | 2024 | [Pub] | |
| Fine-tuning federasi model bahasa besar di bawah tugas yang heterogen dan sumber daya klien | Neurips | 2024 | [Pub] | |
| Personalisasi penyempurnaan dalam pembelajaran federasi untuk mengurangi klien permusuhan | Neurips | 2024 | [Pub] | |
| Meninjau kembali ensembling dalam pembelajaran federasi satu kali | Neurips | 2024 | [Pub] | |
| Fedllm-Bench: tolok ukur realistis untuk pembelajaran federasi model bahasa besar | Neurips | 2024 | [Pub] | |
| $ exttt {pfl-research} $: kerangka kerja simulasi untuk mempercepat penelitian dalam pembelajaran federasi pribadi | Neurips | 2024 | [Pub] | |
| FedMeki: Benchmark untuk Menskalakan Model Yayasan Medis melalui Injeksi Pengetahuan Federasi | Neurips | 2024 | [Pub] | |
| Perkiraan momentum dalam pembelajaran federasi pribadi asinkron | Lokakarya Neurips | 2024 | [Pub] | |
| Cohort Squeeze: Di luar satu putaran komunikasi tunggal per kohort dalam pembelajaran federasi lintas-perangkat | Lokakarya Neurips | 2024 | [Pub] | |
| Pembelajaran federasi dengan konten generatif | Lokakarya Neurips | 2024 | [Pub] | |
| Memanfaatkan data teks yang tidak terstruktur untuk penyetelan instruksi federasi dari model bahasa besar | Lokakarya Neurips | 2024 | [Pub] | |
| Serangan keselamatan yang muncul dan pertahanan dalam penyetelan instruksi federasi dari model bahasa besar | Lokakarya Neurips | 2024 | [Pub] | |
| Kolaborasi bebas pembelotan antara pesaing dalam sistem pembelajaran | Lokakarya Neurips | 2024 | [Pub] | |
| Tentang tingkat konvergensi dari pembelajaran Q gabungan di seluruh lingkungan yang heterogen | Lokakarya Neurips | 2024 | [Pub] | |
| Enccluster: Membawa enkripsi fungsional dalam model dasar federasi | Lokakarya Neurips | 2024 | [Pub] | |
| Ferret: penyetelan parameter penuh federasi pada skala untuk model bahasa besar | Lokakarya Neurips | 2024 | [Pub] | |
| Pembelajaran federasi yang bisa dicolokkan | Lokakarya Neurips | 2024 | [Pub] | |
| Pelatihan rendah dinamis federasi dengan jaminan konvergensi kerugian global | Lokakarya Neurips | 2024 | [Pub] | |
| Masa depan model bahasa besar pra-pelatihan adalah federasi | Lokakarya Neurips | 2024 | [Pub] | |
| Pembelajaran kolaboratif dengan representasi linier bersama: tarif statistik dan algoritma optimal | Lokakarya Neurips | 2024 | [Pub] | |
| Fenomena sinaptikal: Ketika semua model yayasan menikahi pembelajaran dan blockchain federasi | Lokakarya Neurips | 2024 | [Pub] | |
| ZoopFL: Menjelajahi Model Yayasan Kotak Hitam untuk Pembelajaran Federasi yang Dipersonalisasi | Lokakarya Neurips | 2024 | [Pub] | |
| DECOMFL: Pembelajaran federasi dengan komunikasi bebas dimensi | Lokakarya Neurips | 2024 | [Pub] | |
| Meningkatkan konektivitas kelompok untuk generalisasi pembelajaran mendalam federasi | Lokakarya Neurips | 2024 | [Pub] | |
| Peta: Model penggabungan dengan bagian depan pareto diamortisasi menggunakan perhitungan terbatas | Lokakarya Neurips | 2024 | [Pub] | |
| OPA: Agregasi pribadi satu-shot dengan interaksi klien tunggal dan aplikasinya untuk pembelajaran federasi | Lokakarya Neurips | 2024 | [Pub] | |
| Pemangkasan model hibrida adaptif dalam pembelajaran federasi melalui eksplorasi kerugian | Lokakarya Neurips | 2024 | [Pub] | |
| Pelatihan Federasi Model Bahasa di Dunia | Lokakarya Neurips | 2024 | [Pub] | |
| Fedstein: Meningkatkan pembelajaran multi-domain federasi melalui penaksir James-Stein | Lokakarya Neurips | 2024 | [Pub] | |
| Meningkatkan penemuan kausal dalam pengaturan federasi dengan sampel lokal terbatas | Lokakarya Neurips | 2024 | [Pub] | |
| $ exttt {pfl-research} $: kerangka kerja simulasi untuk mempercepat penelitian dalam pembelajaran federasi pribadi | Lokakarya Neurips | 2024 | [Pub] | |
| DMM: Mekanisme Matriks Terdistribusi untuk Pembelajaran Federasi yang Berbeda-Privat Menggunakan Berbagi Rahasia Berkemas | Lokakarya Neurips | 2024 | [Pub] | |
| FedCBO: mencapai konsensus kelompok dalam pembelajaran federasi yang dikelompokkan melalui optimasi berbasis konsensus | Jmlr | 2024 | [Pub] | |
| Pencocokan grafik federasi yang efektif | ICML | 2024 | [Pub] | |
| Memahami pembelajaran federasi yang dibantu server di hadapan partisipasi klien yang tidak lengkap | ICML | 2024 | [Pub] | |
| Beyond the Federation: Topologi-sadar belajar federasi untuk generalisasi kepada klien yang tidak terlihat | ICML | 2024 | [Pub] | |
| FedBPT: Tuning prompt kotak hitam federasi yang efisien untuk model bahasa besar | ICML | 2024 | [Pub] | |
| Bridging Model Heterogenitas dalam Pembelajaran Federasi Melalui Pembelajaran Timbal Uang Tinggi Berbasis Ketidakpastian | ICML | 2024 | [Pub] | |
| Perspektif teoritis baru tentang heterogenitas data dalam optimasi federasi | ICML | 2024 | [Pub] | |
| Meningkatkan penyimpanan dan efisiensi komputasi dalam pembelajaran multimodal federasi untuk model skala besar | ICML | 2024 | [] | |
| Momentum untuk Kemenangan: Pembelajaran Penguatan Federasi Kolaboratif Di Lingkungan Heterogen | ICML | 2024 | [Pub] | |
| Pembelajaran Federated Byzantine-Robust: Dampak subsampling klien dan pembaruan lokal | ICML | 2024 | [Pub] | |
| Manfaat yang dapat dibuktikan dari langkah -langkah lokal dalam pembelajaran federasi yang heterogen untuk jaringan saraf: perspektif pembelajaran fitur | ICML | 2024 | [Pub] | |
| Mempercepat pembelajaran federasi dengan estimasi rata -rata yang terdistribusi cepat | ICML | 2024 | [Pub] | |
| Pembelajaran federasi yang adil melalui inti veto proporsional | ICML | 2024 | [Pub] | |
| AEGISFL: Pembelajaran federasi biztine-silo yang efisien dan fleksibel | ICML | 2024 | [Pub] | |
| Memulihkan label dari pembaruan lokal dalam pembelajaran federasi | ICML | 2024 | [Pub] | |
| FedMbridge: Pembelajaran Federasi Multimodal Bridgeable | ICML | 2024 | [Pub] | |
| Menyelaraskan generalisasi dan personalisasi dalam pembelajaran cepat federasi | ICML | 2024 | [Pub] | |
| Perkiraan gangguan global lokal lebih baik daripada gangguan lokal untuk minimalisasi ketajaman yang diarahkan oleh federasi | ICML | 2024 | [Pub] | |
| Mempercepat pembelajaran federasi yang heterogen dengan pengklasifikasi bentuk tertutup | ICML | 2024 | [Pub] | |
| Federated Combinatorial Multi-Agent Multi-Armed Bandit | ICML | 2024 | [Pub] | |
| Metode keturunan gradien komposisi stokastik rekursif dua kali lipat untuk optimasi komposisi multi-level federasi | ICML | 2024 | [Pub] | |
| Pembelajaran federasi heterogen pribadi tanpa server tepercaya ditinjau kembali: algoritma kesalahan-optimal dan komunikasi-efisien untuk kerugian cembung | ICML | 2024 | [Pub] | |
| FedRC: Mengatasi Beragam Distribusi Pergeseran Tantangan dalam Pembelajaran Federasi dengan Clustering yang Kokoh | ICML | 2024 | [Pub] | |
| Mengejar kesejahteraan keseluruhan dalam pembelajaran federasi melalui pengambilan keputusan berurutan | ICML | 2024 | [Pub] | |
| Pra-teks: Model bahasa pelatihan tentang data federasi pribadi di zaman LLMS | ICML | 2024 | [Pub] | |
| Agregasi entropi yang digerakkan sendiri untuk Pembelajaran Federasi Heterogen Bizantium-Bizantium Heterogen | ICML | 2024 | [Pub] | |
| Mengatasi data dan model heterogenitas dalam pembelajaran federasi yang terdesentralisasi melalui jangkar sintetis | ICML | 2024 | [Pub] | |
| Optimalisasi federasi dengan koreksi drift yang diatur ganda | ICML | 2024 | [Pub] | |
| FedSC: Pembelajaran yang dapat di-federasi yang dapat dibuktikan dengan tujuan kontras spektral dibandingkan data non-IID | ICML | 2024 | [Pub] | |
| Prediksi konformal federasi Byzantine-Robust yang disertifikasi | ICML | 2024 | [Pub] | |
| Mencapai sparsifikasi gradien lossless melalui pemetaan ke ruang alternatif dalam pembelajaran federasi | ICML | 2024 | [Pub] | |
| Pembelajaran federasi yang dikelompokkan melalui partisi berbasis gradien | ICML | 2024 | [Pub] | |
| Pintu Keluar Awal Berulang Untuk Pembelajaran Federasi dengan Klien yang Heterogen | ICML | 2024 | [Pub] | |
| Memikirkan kembali pencarian minimum datar dalam pembelajaran federasi | ICML | 2024 | [Pub] | |
| FedBat: Pembelajaran federasi yang hemat komunikasi melalui binarisasi yang dapat dipelajari | ICML | 2024 | [Pub] | |
| Pembelajaran perwakilan federasi dalam rezim yang kurang parameterisasi | ICML | 2024 | [Pub] | |
| FedLMT: Tackling System Heterogenitas pembelajaran federasi melalui pelatihan model peringkat rendah dengan jaminan teoretis | ICML | 2024 | [Pub] | |
| Algoritma sadar-noise untuk pembelajaran federasi pribadi yang heterogen diferensial | ICML | 2024 | [Pub] | |
| Perak: Pengurangan Varians Loop Tunggal dan Aplikasi untuk Pembelajaran Federasi | ICML | 2024 | [Pub] | |
| Signsgd dengan pertahanan federasi: memanfaatkan serangan permusuhan melalui decoding tanda gradien | ICML | 2024 | [Pub] | |
| FedCal: Mencapai Kalibrasi Lokal dan Global dalam Pembelajaran Federasi melalui Parameter Parameterisasi Agregat | ICML | 2024 | [Pub] | |
| Pembelajaran berkelanjutan federasi melalui transfer pengetahuan ganda berbasis prompt | ICML | 2024 | [Pub] | |
| Penyetelan parameter penuh federasi dari model bahasa miliaran dengan biaya komunikasi di bawah 18 kilobyte | ICML | 2024 | [Pub] | |
| Maksimalisasi submodular yang dapat diuraikan dalam pengaturan federasi | ICML | 2024 | [Pub] | |
| Optimalisasi Cembung Stokastik Pribadi dan Federasi: Strategi Efisien untuk Sistem Terpusat | ICML | 2024 | [Pub] | |
| Pemodelan yang lebih baik dari kumpulan data federasi menggunakan campuran-diririchlet-multinomials | ICML | 2024 | [Pub] | |
| Pelajaran dari Analisis Kesalahan Generalisasi Pembelajaran Federasi: Anda dapat berkomunikasi lebih jarang! | ICML | 2024 | [Pub] | |
| Pembelajaran beberapa-shot Bizantium tangguh dan cepat federasi cepat | ICML | 2024 | [Pub] | |
| Pembelajaran invarian federasi yang dimotivasi secara kausal dengan regularisasi informasi-informasi yang menghindari jalan pintas | ICML | 2024 | [Pub] | |
| Pemilihan imitasi klien berbasis peringkat untuk pembelajaran federasi yang efisien | ICML | 2024 | [Pub] | |
| Menuju teori pembelajaran federasi yang tidak diawasi: analisis non-asymptotic algoritma EM federasi | ICML | 2024 | [Pub] | |
| Fadas: Menuju optimasi asinkron adaptif federasi | ICML | 2024 | [Pub] | |
| Pembelajaran Penguatan Offline Federasi: Cakupan Kebijakan Tunggal Kolaboratif Cukup | ICML | 2024 | [Pub] | |
| FedReDefense: Membela terhadap serangan keracunan model untuk pembelajaran federasi menggunakan kesalahan pemutakhiran model kesalahan rekonstruksi model | ICML | 2024 | [Pub] | |
| MH-PFLID: Model pembelajaran federasi yang dipersonalisasi secara pribadi melalui injeksi dan distilasi untuk analisis data medis | ICML | 2024 | [Pub] | |
| Pembelajaran Neuro-Symbolic Federasi | ICML | 2024 | [Pub] | |
| Personalisasi Kelompok Adaptif untuk Pembelajaran Mutual Transfer Federated | ICML | 2024 | [Pub] | |
| Menyeimbangkan kesamaan dan saling melengkapi untuk pembelajaran federasi | ICML | 2024 | [Pub] | |
| GNN yang Federated sendiri dengan augmentasi anti-shortcut | ICML | 2024 | [Pub] | |
| Algoritma minimax multi-level stokastik federasi untuk maksimalisasi AUC yang dalam | ICML | 2024 | [Pub] | |
| Coala: Platform Pembelajaran Federasi Praktis dan Berpusat pada Visi | ICML | 2024 | [Pub] | |
| Pembelajaran federasi vertikal asinkron yang aman dan cepat melalui optimasi hibrida bertingkat | Mach Learn | 2024 | [Pub] | |
| Pembelajaran federasi berkelompok berkompleks berkomunikasi melalui jarak model | USTC; Laboratorium kunci intelijen kognitif negara bagian | Mach Learn | 2024 | [Pub] |
| Pembelajaran federasi dengan agregasi superquantile untuk data heterogen. | Penelitian Google | Mach Learn | 2024 | [Pub] [PDF] [Kode] |
| Menyelaraskan output model untuk kelas pembelajaran non-IID yang tidak seimbang | Nju | Mach Learn | 2024 | [Pub] |
| Pembelajaran federasi dari jaringan kausal linier umum | Tpami | 2024 | [Pub] | |
| Cross-modal Federated Human Activity Recognition | Tpami | 2024 | [Pub] | |
| Proses Gaussian Federasi: Konvergensi, Personalisasi Otomatis dan Pemodelan Multi-Fidelity | Universitas Northeastern; Uom | Tpami | 2024 | [Pub] [PDF] [Kode] |
| Dampak serangan permusuhan pada pembelajaran federasi: survei | IIT | Tpami | 2024 | [Pub] |
| Memahami dan meringankan keruntuhan dimensi dalam pembelajaran federasi | Nus | Tpami | 2024 | [Pub] [PDF] [Kode] |
| No One Left Behind: Pembelajaran Inkremental Kelas Federasi Dunia Nyata | CAS; UCAS | Tpami | 2024 | [Pub] [PDF] [Kode] |
| Generalizable Heterogen Federated Cross-Correlation dan Instance Kesamaan Pembelajaran | Whu | Tpami | 2024 | [Pub] [PDF] [Kode] |
| Pembelajaran federasi asinkron multi-tahap dengan privasi diferensial adaptif | HPU; Xjtu | Tpami | 2024 | [Pub] [PDF] [Kode] |
| Kerangka Belajar Federasi Bayesian dengan Perkiraan Laplace Online | Sustech | Tpami | 2024 | [Pub] [PDF] [Kode] |
| Meningkatkan pembelajaran federasi satu-shot melalui data dan penambah bersama ensemble | USTC; Hkbu | Iclr | 2024 | [Pub] |
| Estimasi Privasi Empiris One-Shot untuk Pembelajaran Federasi | Iclr | 2024 | [Pub] [PDF] | |
| Rata -rata terkontrol stokastik untuk pembelajaran federasi dengan kompresi komunikasi | LinkedIn; Upenn | Iclr | 2024 | [Pub] [PDF] |
| Metode ringan untuk mengatasi statistik partisipasi yang tidak diketahui dalam rata -rata federasi | IBM | Iclr | 2024 | [Pub] [PDF] [Kode] |
| Perspektif informasi timbal balik tentang pembelajaran kontras federasi | Qualcomm | Iclr | 2024 | [Pub] |
| Algoritma Benchmarking untuk Generalisasi Domain Federasi | Universitas Purdue | Iclr | 2024 | [Pub] [PDF] [Kode] |
| Pembelajaran pohon federasi yang efektif dan efisien pada data hibrida | UC Berkeley | Iclr | 2024 | [Pub] [PDF] |
| Rekomendasi federasi dengan personalisasi aditif | UTS | Iclr | 2024 | [Pub] [PDF] [Kode] |
| Menangani heterogenitas data dalam pembelajaran federasi yang tidak sinkron dengan kalibrasi pembaruan yang di -cache | PSU | Iclr | 2024 | [Pub] [Supp] |
| Pelatihan Orthogonal Federasi: Mitigasi Global Catastrophic Forgetting dalam Pembelajaran Federasi Berkelanjutan | USC | Iclr | 2024 | [Pub] [Supp] [PDF] |
| Lupa akurat untuk pembelajaran berkelanjutan yang heterogen | KAMIS | Iclr | 2024 | [Pub] [kode] |
| Penemuan kausal federasi dari data heterogen | Mbzuai | Iclr | 2024 | [Pub] [PDF] [Kode] |
| Pada bandit kontekstual linear federasi pribadi yang berbeda | Wayne State University | Iclr | 2024 | [Pub] [Supp] [PDF] |
| Memberi insentif kepada komunikasi yang jujur untuk bandit federasi | Universitas Virginia | Iclr | 2024 | [Pub] [PDF] |
| Adaptasi domain federasi berprinsip: proyeksi gradien dan peninggalan otomatis | UIUC | Iclr | 2024 | [Pub] |
| FedP3: Pemangkasan jaringan yang dipersonalisasi dan ramah privasi di bawah heterogenitas model | Kaust | Iclr | 2024 | [Pub] |
| Generasi cepat yang digerakkan oleh teks untuk model bahasa penglihatan dalam pembelajaran federasi | Robert Bosch LLC | Iclr | 2024 | [Pub] [PDF] |
| Meningkatkan Lora dalam Pembelajaran Federasi Privasi | Universitas Northeastern | Iclr | 2024 | [Pub] |
| Fedwon: MEMBAWA MULTI-DOMAIN MULTIAB Federated Learning Tanpa Normalisasi | Sony AI | Iclr | 2024 | [Pub] [PDF] |
| FedTrans: Estimasi Utilitas Transparan Klien untuk Pembelajaran Federasi yang Kuat | Tu Delft | Iclr | 2024 | [Pub] |
| FedCompass: Pembelajaran federasi silo-silo yang efisien pada perangkat klien yang heterogen menggunakan penjadwal yang sadar akan daya komputasi | Anl; UIUC; NCSA | Iclr | 2024 | [Pub] [PDF] [Kode] [Halaman] |
| Optimalisasi Coreset Bayesian untuk Pembelajaran Federasi yang Dipersonalisasi | IIT Bombay | Iclr | 2024 | [Pub] |
| Konektivitas mode linier lapisan bijaksana | Ruhr-Universtät Bochum | Iclr | 2024 | [Pub] [PDF] [Supp] |
| Palsu sampai Make It: Federated Learning dengan generasi yang berorientasi konsensus | Sjtu | Iclr | 2024 | [Pub] [PDF] |
| Bersembunyi di depan mata: menyamarkan data mencuri serangan dalam pembelajaran federasi | Insait | Iclr | 2024 | [Pub] [Supp] [PDF] |
| Analisis waktu terbatas dari pembelajaran penguatan federasi heterogen on-policy | Universitas Columbia | Iclr | 2024 | [Pub] [PDF] |
| Pembelajaran federasi adaptif dengan klien yang disetel otomatis | Universitas Beras | Iclr | 2024 | [Pub] [Supp] [PDF] |
| Belajar gabungan pintu belakang dengan meracuni lapisan kritis-backdoor | tidak | Iclr | 2024 | [Pub] [Supp] [PDF] |
| Federated Q-Learning: Linear Regret Speedup dengan biaya komunikasi yang rendah | PSU | Iclr | 2024 | [Pub] [Supp] [PDF] |
| FedImpro: Mengukur dan Meningkatkan Pembaruan Klien dalam Pembelajaran Federasi | Hkbu | Iclr | 2024 | [Pub] [PDF] |
| Jarak federasi Wasserstein | MIT | Iclr | 2024 | [Pub] [Supp] [PDF] |
| Analisis yang lebih baik dari kliping per sampel dan per update dalam pembelajaran federasi | DTU | Iclr | 2024 | [Pub] |
| Fedcda: Pembelajaran federasi dengan agregasi silang-sadar-divergensi | Ntu | Iclr | 2024 | [Pub] [Supp] |
| Gradien lintas-lapisan internal untuk memperluas homogenitas ke heterogenitas dalam pembelajaran federasi | Hku | Iclr | 2024 | [Pub] [PDF] |
| Manfaat Momentum Non-IID Federated Learning secara sederhana dan terbukti | PKU; Upenn | Iclr | 2024 | [Pub] [PDF] |
| Optimalisasi bandit non-linear yang efisien komunikasi | Universitas Yale | Iclr | 2024 | [Pub] [PDF] |
| Penilaian kontribusi yang adil dan efisien untuk pembelajaran federasi vertikal | Huawei | Iclr | 2024 | [Pub] [Supp] [PDF] [Kode] |
| Demistifikal pertukaran keadilan lokal & global dalam pembelajaran federasi menggunakan dekomposisi informasi parsial | UMCP | Iclr | 2024 | [Pub] [PDF] |
| Mempelajari representasi yang tidak berubah secara pribadi untuk klien federasi yang heterogen | Polyu | Iclr | 2024 | [Pub] |
| Pefll: Pembelajaran Federasi yang Dipersonalisasi dengan Belajar Belajar | Ist | Iclr | 2024 | [PUB] [SUPP] [PDF] |
| Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates | JHU | ICLR | 2024 | [PUB] [SUPP] [PDF] |
| FedInverse: Evaluating Privacy Leakage in Federated Learning | USQ | ICLR | 2024 | [PUB] [SUPP] |
| FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization | UMCP | ICLR | 2024 | [PUB] [SUPP] [PDF] |
| Robust Training of Federated Models with Extremely Label Deficiency | HKBU | ICLR | 2024 | [PUB] [PDF] [CODE] |
| Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory | RIKEN AIP | ICLR | 2024 | [PUB] |
| Teach LLMs to Phish: Stealing Private Information from Language Models | Princeton University | ICLR | 2024 | [PUB] |
| Like Oil and Water: Group Robustness Methods and Poisoning Defenses Don't Mix | UMCP | ICLR | 2024 | [PUB] |
| Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise | HKUST | ICLR | 2024 | [PUB] [PDF] |
| Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks | KAS | ICLR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Local Composite Saddle Point Optimization | Purdue University | ICLR | 2024 | [PUB] [PDF] |
| Enhancing Neural Training via a Correlated Dynamics Model | TIIT | ICLR | 2024 | [PUB] [PDF] |
| EControl: Fast Distributed Optimization with Compression and Error Control | Saarland University | ICLR | 2024 | [PUB] [SUPP] [PDF] |
| Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit | HKUST | ICLR | 2024 | [PUB] |
| FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent | UMCP | ICLR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate | CUHK | ICLR | 2024 | [PUB] |
| Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages | University of Cambridge | ICLR | 2024 | [PUB] |
| Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity | NTT DATA Mathematical Systems Inc. | ICLR | 2024 | [PUB] |
| VFLAIR: A Research Library and Benchmark for Vertical Federated Learning | KAMIS | ICLR | 2024 | [PUB] [PDF] [CODE] |
| Incentive-Aware Federated Learning with Training-Time Model Rewards | NUS | ICLR | 2024 | [PUB] [SUPP] |
| VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks | NUS | ICLR | 2024 | [PUB] [PDF] [CODE] |
| FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data | ZJU | ICLR | 2024 | [PUB] [SUPP] [PDF] |
| SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning | University at Buffalo | NeurIPS | 2023 | [PUB] [PDF] [SUPP] |
| Mechanism Design for Collaborative Normal Mean Estimation | UW-Madison | NeurIPS | 2023 | [PUB] [PDF] |
| Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity | EPFL | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization | UIUC | NeurIPS | 2023 | [PUB] [SUPP] |
| Convergence Analysis of Sequential Federated Learning on Heterogeneous Data | BUPT | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition | MBZUAI | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Private Federated Frequency Estimation: Adapting to the Hardness of the Instance | JHU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization | Rutgers University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Incentivized Communication for Federated Bandits | University of Virginia | NeurIPS | 2023 | [PUB] [PDF] |
| Multiply Robust Federated Estimation of Targeted Average Treatment Effects | Northeastern University | NeurIPS | 2023 | [PUB] [PDF] |
| IBA: Towards Irreversible Backdoor Attacks in Federated Learning | Vanderbilt University; VinUniversity | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning | KAIST | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Federated Linear Bandits with Finite Adversarial Actions | University of Virginia | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FedNAR: Federated Optimization with Normalized Annealing Regularization | MBZUAI | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Guiding The Last Layer in Federated Learning with Pre-Trained Models | Concordia University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization | HZAU | NeurIPS | 2023 | [PUB] [SUPP] |
| Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection | KAIST | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks | USC | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning | UTS | NeurIPS | 2023 | [PUB] [SUPP] |
| One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning | Rice University | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training | Gatech | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning | PSU; UIUC | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Towards Personalized Federated Learning via Heterogeneous Model Reassembly | PSU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction | GWU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning | ECNU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning | Western University | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks | Xidian University; University of Guelph; Zhejiang Key Laboratory of Multi-dimensional Perception Technology, Application and Cybersecurity | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Federated Learning with Bilateral Curation for Partially Class-Disjoint Data | SJTU; Shanghai AI Laboratory | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds | GMU; SJTU | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| FedL2P: Federated Learning to Personalize | University of Cambridge; Samsung AI Center | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Adaptive Test-Time Personalization for Federated Learning | UIUC | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| Federated Conditional Stochastic Optimization | University of Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Spectral Clustering via Secure Similarity Reconstruction | CUHK | NeurIPS | 2023 | [PUB] |
| Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM | UM-Dearborn | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks | CMU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Multi-Objective Learning | RIT | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout | University of British Columbia; Gatech | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning | University of Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] |
| Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems | University of Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| StableFDG: Style and Attention Based Learning for Federated Domain Generalization | KAIST; Purdue University | NeurIPS | 2023 | [PUB] [PDF] |
| Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization | The University of Sydney | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| DELTA: Diverse Client Sampling for Fasting Federated Learning | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Federated Compositional Deep AUC Maximization | Temple University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning | PSU | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Flow: Per-instance Personalized Federated Learning | University of Massachusetts | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Eliminating Domain Bias for Federated Learning in Representation Space | SJTU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Learning with Manifold Regularization and Normalized Update Reaggregation | SEDIKIT | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Structured Federated Learning through Clustered Additive Modeling | University of Technology Sydney | NeurIPS | 2023 | [PUB] [SUPP] |
| Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer | ZJU; Singapore University of Technology and Design | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Dynamic Personalized Federated Learning with Adaptive Differential Privacy | WHU | NeurIPS | 2023 | [PUB] [SUPP] [CODE] |
| Fed-CO$_{2}$ : Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning | ShanghaiTech University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Solving a Class of Non-Convex Minimax Optimization in Federated Learning | University of Pittsburgh | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Learning via Meta-Variational Dropout | SNU | NeurIPS | 2023 | [PUB] [CODE] |
| Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates | Purdue University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning | NTU | NeurIPS | 2023 | [PUB] [CODE] |
| Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense | PKU; Tencent | NeurIPS | 2023 | [PUB] [SUPP] |
| FedFed: Feature Distillation against Data Heterogeneity in Federated Learning | BUAA; HKBU | NeurIPS | 2023 | [PUB] [PDF] [CODE] |
| PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning | SCU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] [解读] |
| Spectral Co-Distillation for Personalized Federated Learning | SUTD | NeurIPS | 2023 | [PUB] |
| Breaking the Communication-Privacy-Accuracy Tradeoff with | ZJU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation | Stanford University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| (Amplified) Banded Matrix Factorization: A unified approach to private training | Google DeepMind | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices | KIT | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation | Stanford University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Incentivizing Honesty among Competitors in Collaborative Learning and Optimization | ETH Zurich | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Resilient Constrained Learning | UPenn | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting | KAUST | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Collaboratively Learning Linear Models with Structured Missing Data | Stanford University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy | EPFL | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Fast Optimal Locally Private Mean Estimation via Random Projections | Apple Inc. | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Contextual Stochastic Bilevel Optimization | EPFL; ETH Zürich | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Understanding Deep Gradient Leakage via Inversion Influence Functions | MSU; Michigan State University; University of Texas at Austin | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Inner Product-based Neural Network Similarity | Purdue University | NeurIPS | 2023 | [PUB] [SUPP] |
| Correlation Aware Sparsified Mean Estimation Using Random Projection | CMU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| TIES-Merging: Resolving Interference When Merging Models | PBB | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data | Purdue University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Large-Scale Distributed Learning via Private On-Device LSH | UMD | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Faster Relative Entropy Coding with Greedy Rejection Coding | University of Cambridge | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization | SJTU | NeurIPS | 2023 | [PUB] [SUPP] |
| Momentum Provably Improves Error Feedback! | ETH AI Center; ETH Zurich | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| Strategic Data Sharing between Competitors | Sofia University | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
| H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets | GMU | NeurIPS | 2023 | [PUB] |
| Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking | Wyze Labs | NeurIPS Datasets and Benchmarks | 2023 | [PUB] [SUPP] [DATASET] |
| Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning | Google Research | NeurIPS Datasets and Benchmarks | 2023 | [PUB] [PDF] [DATASET] |
| Text-driven Prompt Generation for Vision-Language Models in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data | NeurIPS workshop | 2023 | [PUB] | |
| FedSoL: Bridging Global Alignment and Local Generality in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| One-shot Empirical Privacy Estimation for Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning | NeurIPS workshop | 2023 | [PUB] | |
| SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models | NeurIPS workshop | 2023 | [PUB] | |
| The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Towards Building the FederatedGPT: Federated Instruction Tuning | NeurIPS workshop | 2023 | [PUB] | |
| Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR | NeurIPS workshop | 2023 | [PUB] | |
| LASER: Linear Compression in Wireless Distributed Optimization | NeurIPS workshop | 2023 | [PUB] | |
| MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization | NeurIPS workshop | 2023 | [PUB] | |
| TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation | NeurIPS workshop | 2023 | [PUB] | |
| An Empirical Evaluation of Federated Contextual Bandit Algorithms | NeurIPS workshop | 2023 | [PUB] | |
| RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation | NeurIPS workshop | 2023 | [PUB] | |
| FDAPT: Federated Domain-adaptive Pre-training for Language Models | NeurIPS workshop | 2023 | [PUB] | |
| Making Batch Normalization Great in Federated Deep Learning | NeurIPS workshop | 2023 | [PUB] | |
| Correlated Noise Provably Beats Independent Noise for Differentially Private Learning | NeurIPS workshop | 2023 | [PUB] | |
| Parameter Averaging Laws for Multitask Language Models | NeurIPS workshop | 2023 | [PUB] | |
| Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages | NeurIPS workshop | 2023 | [PUB] | |
| Beyond Parameter Averaging in Model Aggregation | NeurIPS workshop | 2023 | [PUB] | |
| Augmenting Federated Learning with Pretrained Transformers | NeurIPS workshop | 2023 | [PUB] | |
| Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization | NeurIPS workshop | 2023 | [PUB] | |
| DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization | NeurIPS workshop | 2023 | [PUB] | |
| Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System | NeurIPS workshop | 2023 | [PUB] | |
| Learning Optimizers for Local SGD | NeurIPS workshop | 2023 | [PUB] | |
| Exploring User-level Gradient Inversion with a Diffusion Prior | NeurIPS workshop | 2023 | [PUB] | |
| User Inference Attacks on Large Language Models | NeurIPS workshop | 2023 | [PUB] | |
| FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis | NeurIPS workshop | 2023 | [PUB] | |
| Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models | NeurIPS workshop | 2023 | [PUB] | |
| Backdoor Threats from Compromised Foundation Models to Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| MOFL/D: A Federated Multi-objective Learning Framework with Decomposition | NeurIPS workshop | 2023 | [PUB] | |
| Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models | NeurIPS workshop | 2023 | [PUB] | |
| FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning | NeurIPS workshop | 2023 | [PUB] | |
| Private and Personalized Histogram Estimation in a Federated Setting | NeurIPS workshop | 2023 | [PUB] | |
| The Aggregation–Heterogeneity Trade-off in Federated Learning | PKU | KUDA JANTAN MUDA | 2023 | [PUB] |
| FLASH: Automating federated learning using CASH | Rensselaer Polytechnic Institute | UAI | 2023 | [PUB] [SUPP] [MATERIAL] |
| Personalized federated domain adaptation for item-to-item recommendation | AWS AI Labs | UAI | 2023 | [PUB] [PDF] [SUPP] [MATERIAL] [CODE] |
| Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning | Baidu Research | UAI | 2023 | [PUB] [PDF] [SUPP] [MATERIAL] |
| Federated learning of models pre-trained on different features with consensus graphs | IBM Research | UAI | 2023 | [PUB] [SUPP] [MATERIAL] [CODE] |
| Fast Heterogeneous Federated Learning with Hybrid Client Selection | NWPU | UAI | 2023 | [PUB] [SUPP] [MATERIAL] [PDF] |
| Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning | Cornell University | UAI | 2023 | [PUB] [PDF] [SUPP] [MATERIAL] [CODE] |
| Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape | The University of Sydney | ICML | 2023 | [PUB] [PDF] [SLIDES] |
| Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation | LinkedIn Ads | ICML | 2023 | [PUB] [PDF] |
| FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization | Grup Alibaba | ICML | 2023 | [PUB] [PDF] [CODE] |
| Federated Conformal Predictors for Distributed Uncertainty Quantification | MIT | ICML | 2023 | [PUB] [PDF] [CODE] |
| Federated Adversarial Learning: A Framework with Convergence Analysis | UBC | ICML | 2023 | [PUB] [PDF] |
| Federated Heavy Hitter Recovery under Linear Sketching | Google Research | ICML | 2023 | [PUB] [PDF] [CODE] |
| Doubly Adversarial Federated Bandits | London School of Economics and Political Science | ICML | 2023 | [PUB] [PDF] [CODE] |
| Achieving Linear Speedup in Non-IID Federated Bilevel Learning | universitas | ICML | 2023 | [PUB] [PDF] |
| One-Shot Federated Conformal Prediction | Université Paris-Saclay | ICML | 2023 | [PUB] [PDF] [CODE] |
| Federated Online and Bandit Convex Optimization | TTIC | ICML | 2023 | [PUB] |
| Federated Linear Contextual Bandits with User-level Differential Privacy | The Pennsylvania State University | ICML | 2023 | [PUB] [PDF] |
| Vertical Federated Graph Neural Network for Recommender System | NUS | ICML | 2023 | [PUB] [PDF] [CODE] |
| Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation | University at Buffalo | ICML | 2023 | [PUB] [PDF] |
| Towards Understanding Ensemble Distillation in Federated Learning | KAIST | ICML | 2023 | [PUB] |
| Personalized Subgraph Federated Learning | KAIST | ICML | 2023 | [PUB] [PDF] [CODE] |
| Conformal Prediction for Federated Uncertainty Quantification Under Label Shift | Lagrange Mathematics and Computing Research Center; CMAP | ICML | 2023 | [PUB] [PDF] |
| Secure Federated Correlation Test and Entropy Estimation | CMU | ICML | 2023 | [PUB] [PDF] |
| Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships | JLU | ICML | 2023 | [PUB] [CODE] |
| Personalized Federated Learning under Mixture of Distributions | Universitas California | ICML | 2023 | [PUB] [PDF] [CODE] |
| FedDisco: Federated Learning with Discrepancy-Aware Collaboration | SJTU | ICML | 2023 | [PUB] [PDF] [CODE] |
| Anchor Sampling for Federated Learning with Partial Client Participation | Purdue University | ICML | 2023 | [PUB] [PDF] [CODE] |
| Private Federated Learning with Autotuned Compression | JHU; Google | ICML | 2023 | [PUB] [PDF] |
| Fast Federated Machine Unlearning with Nonlinear Functional Theory | Universitas Auburn | ICML | 2023 | [PUB] |
| On the Convergence of Federated Averaging with Cyclic Client Participation | CMU | ICML | 2023 | [PUB] [PDF] |
| Revisiting Weighted Aggregation in Federated Learning with Neural Networks | ZJU | ICML | 2023 | [PUB] [PDF] [CODE] |
| The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond | CMU | ICML | 2023 | [PUB] [PDF] [SLIDES] |
| GuardHFL: Privacy Guardian for Heterogeneous Federated Learning | UESTC; NTU | ICML | 2023 | [PUB] |
| Flash: Concept Drift Adaptation in Federated Learning | University of Massachusetts | ICML | 2023 | [PUB] |
| DoCoFL: Downlink Compression for Cross-Device Federated Learning | VMware Research; Technion | ICML | 2023 | [PUB] [PDF] |
| FeDXL: Provable Federated Learning for Deep X-Risk Optimization | Texas A&M University | ICML | 2023 | [PUB] [PDF] [CODE] |
| No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation | MEMUKUL | ICML | 2023 | [PUB] [CODE] |
| Personalized Federated Learning with Inferred Collaboration Graphs | SJTU | ICML | 2023 | [PUB] [CODE] |
| Optimizing the Collaboration Structure in Cross-Silo Federated Learning | UIUC | ICML | 2023 | [PUB] [PDF] [CODE] [SLIDES] |
| TabLeak: Tabular Data Leakage in Federated Learning | ETH Zurich | ICML | 2023 | [PUB] [PDF] [CODE] |
| FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization | SJTU | ICML | 2023 | [PUB] [CODE] |
| Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction | Duke University | ICML | 2023 | [PUB] [PDF] |
| Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design | Meta AI | ICML | 2023 | [PUB] [PDF] [CODE] |
| SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning | Owkin Inc. | ICML | 2023 | [PUB] [PDF] [CODE] |
| Improving the Model Consistency of Decentralized Federated Learning | KAMIS | ICML | 2023 | [PUB] [PDF] |
| Efficient Personalized Federated Learning via Sparse Model-Adaptation | Grup Alibaba | ICML | 2023 | [PUB] [PDF] [CODE] |
| From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning | Univ. Lille | ICML | 2023 | [PUB] [PDF] [CODE] |
| LeadFL: Client Self-Defense against Model Poisoning in Federated Learning | TUD | ICML | 2023 | [PUB] [CODE] |
| Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning | HKUST | ICML | 2023 | [PUB] [PDF] [CODE] |
| FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models | HKUST | ICML | 2023 | [PUB] [PDF] |
| FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction | CUHK; The Shenzhen Institute of Artificial Intelligence and Robotics for Society | ICML | 2023 | [PUB] [PDF] [CODE] |
| Towards Unbiased Training in Federated Open-world Semi-supervised Learning | PolyU | ICML | 2023 | [PUB] [PDF] [SLIDES] |
| Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis | Georgia Tech; Meta AI | ICML | 2023 | [PUB] [PDF] |
| Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning | KU Leuven | ICML | 2023 | [PUB] [PDF] [CODE] |
| Fair yet Asymptotically Equal Collaborative Learning | NUS | ICML | 2023 | [PUB] [PDF] [CODE] |
| Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability | Adobe Research | ICML | 2023 | [PUB] [PDF] |
| Adversarial Collaborative Learning on Non-IID Features | UC Berkeley; NUS | ICML | 2023 | [PUB] |
| XTab: Cross-table Pretraining for Tabular Transformers | EPFL; Cornell University; AWS | ICML | 2023 | [PUB] [PDF] [CODE] |
| Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions | NUDT | ICML | 2023 | [PUB] |
| Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting | Key Lab of Intelligent Computing Based Big Data of Zhejiang Province; ZJU; Sony Al | ICML | 2023 | [PUB] [PDF] [CODE] |
| LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning | Rensselaer Polytechnic Institute | ICML | 2023 | [PUB] [PDF] |
| FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks | University of Minnesota | ICML | 2023 | [PUB] |
| Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm | University of Chicago | ICML | 2023 | [PUB] [PDF] [CODE] |
| Ensemble and continual federated learning for classification tasks. | Universidade de Santiago de Compostela | Mach Learn | 2023 | [PUB] [PDF] |
| FAC-fed: Federated adaptation for fairness and concept drift aware stream classification | Leibniz University of Hannover | Mach Learn | 2023 | [PUB] |
| Robust federated learning under statistical heterogeneity via hessian-weighted aggregation | Deakin University | Mach Learn | 2023 | [PUB] |
| FedLab: A Flexible Federated Learning Framework | UESTC; Peng Cheng Lab | JMLR | 2023 | [PUB] [PDF] [CODE] |
| Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training? | JMLR | 2023 | [PUB] | |
| Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning | TAMU | JMLR | 2023 | [PUB] [PDF] [CODE] |
| A First Look into the Carbon Footprint of Federated Learning | University of Cambridge | JMLR | 2023 | [PUB] [PDF] |
| Attacks against Federated Learning Defense Systems and their Mitigation | The University of Newcastle | JMLR | 2023 | [PUB] [CODE] |
| A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates | Universit ́e Cˆ ote d'Azur | JMLR | 2023 | [PUB] [PDF] [CODE] |
| Tighter Regret Analysis and Optimization of Online Federated Learning | Hanyang University | TPAMI | 2023 | [PUB] [PDF] |
| Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup | University of Sydney | TPAMI | 2023 | [PDF] |
| Federated Learning Via Inexact ADMM. | BJTU | TPAMI | 2023 | [PUB] [PDF] [CODE] |
| FedIPR: Ownership Verification for Federated Deep Neural Network Models | SJTU | TPAMI | 2023 | [PUB] [PDF] [CODE] [解读] |
| Decentralized Federated Averaging | NUDT | TPAMI | 2023 | [PUB] [PDF] |
| Personalized Federated Learning with Feature Alignment and Classifier Collaboration | KAMIS | ICLR | 2023 | [PUB] [CODE] |
| MocoSFL: enabling cross-client collaborative self-supervised learning | ASU | ICLR | 2023 | [PUB] [CODE] |
| Single-shot General Hyper-parameter Optimization for Federated Learning | IBM | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated | ICLR | 2023 | [PUB] [PDF] [CODE] | |
| FedExP: Speeding up Federated Averaging via Extrapolation | CMU | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection | MSU | ICLR | 2023 | [PUB] [CODE] |
| DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity | KAUST | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Machine Unlearning of Federated Clusters | University of Illinois | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Federated Neural Bandits | NUS | ICLR | 2023 | [PUB] [PDF] [CODE] |
| FedFA: Federated Feature Augmentation | ETH Zurich | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach | CMU | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Better Generative Replay for Continual Federated Learning | University of Virginia | ICLR | 2023 | [PUB] [CODE] |
| Federated Learning from Small Datasets | IKIM | ICLR | 2023 | [PUB] [PDF] |
| Federated Nearest Neighbor Machine Translation | USTC | ICLR | 2023 | [PUB] [PDF] |
| Meta Knowledge Condensation for Federated Learning | A*STAR | ICLR | 2023 | [PUB] [PDF] |
| Test-Time Robust Personalization for Federated Learning | EPFL | ICLR | 2023 | [PUB] [PDF] [CODE] |
| DepthFL : Depthwise Federated Learning for Heterogeneous Clients | SNU | ICLR | 2023 | [PUB] |
| Towards Addressing Label Skews in One-Shot Federated Learning | NUS | ICLR | 2023 | [PUB] [CODE] |
| Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning | NUS | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation | UMD | ICLR | 2023 | [PUB] [CODE] |
| SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication | UMD | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses | USC | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Effective passive membership inference attacks in federated learning against overparameterized models | Purdue University | ICLR | 2023 | [PUB] |
| FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification | University of Cambridge | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Multimodal Federated Learning via Contrastive Representation Ensemble | KAMIS | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Faster federated optimization under second-order similarity | Princeton University | ICLR | 2023 | [PUB] [PDF] [CODE] |
| FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy | University of Sydney | ICLR | 2023 | [PUB] [CODE] |
| The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation | utexas | ICLR | 2023 | [PUB] [PDF] [CODE] |
| PerFedMask: Personalized Federated Learning with Optimized Masking Vectors | UBC | ICLR | 2023 | [PUB] [CODE] |
| EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data | GMU | ICLR | 2023 | [PUB] [CODE] |
| FedDAR: Federated Domain-Aware Representation Learning | Harvard | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning | upenn | ICLR | 2023 | [PUB] [CODE] |
| FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning | Purdue University | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses | RUC | ICLR | 2023 | [PUB] |
| Efficient Federated Domain Translation | Purdue University | ICLR | 2023 | [PUB] [CODE] |
| On the Importance and Applicability of Pre-Training for Federated Learning | OSU | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models | UMD | ICLR | 2023 | [PUB] [PDF] [CODE] |
| A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy | Universitas California | ICLR | 2023 | [PUB] [PDF] |
| Instance-wise Batch Label Restoration via Gradients in Federated Learning | BUAA | ICLR | 2023 | [PUB] [CODE] |
| Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity | College of William and Mary | ICLR | 2023 | [PUB] |
| CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning | University of Warwick | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Sparse Random Networks for Communication-Efficient Federated Learning | Stanford | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Combating Exacerbated Heterogeneity for Robust Decentralized Models | HKBU | ICLR | 2023 | [PUB] [CODE] |
| Hyperparameter Optimization through Neural Network Partitioning | University of Cambridge | ICLR | 2023 | [PUB] [PDF] |
| Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision? | MIT | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top | mbzuai | ICLR | 2023 | [PUB] [PDF] [CODE] |
| Dual Diffusion Implicit Bridges for Image-to-Image Translation | Stanford | ICLR | 2023 | [PUB] [PDF] [CODE] |
| An accurate, scalable and verifiable protocol for federated differentially private averaging | INRIA Lille | Mach Learn | 2022 | [PUB] [PDF] |
| Federated online clustering of bandits. | CUHK | UAI | 2022 | [PUB] [PDF] [CODE] |
| Privacy-aware compression for federated data analysis. | Meta AI | UAI | 2022 | [PUB] [PDF] [CODE] |
| Faster non-convex federated learning via global and local momentum. | UTEXAS | UAI | 2022 | [PUB] [PDF] |
| Fedvarp: Tackling the variance due to partial client participation in federated learning. | CMU | UAI | 2022 | [PUB] [PDF] |
| SASH: Efficient secure aggregation based on SHPRG for federated learning | CAS; CASTEST | UAI | 2022 | [PUB] [PDF] |
| Bayesian federated estimation of causal effects from observational data | NUS | UAI | 2022 | [PUB] [PDF] |
| Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning | Hanyang University | TPAMI | 2022 | [PUB] |
| Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning | ZJU | TPAMI | 2022 | [PUB] [CODE] |
| Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox | Moscow Institute of Physics and Technology | NeurIPS | 2022 | [PUB] [PDF] |
| LAMP: Extracting Text from Gradients with Language Model Priors | ETHZ | NeurIPS | 2022 | [PUB] [CODE] |
| FedAvg with Fine Tuning: Local Updates Lead to Representation Learning | utexas | NeurIPS | 2022 | [PUB] [PDF] |
| On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond | NUIST | NeurIPS | 2022 | [PUB] [PDF] |
| Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams | WISC | NeurIPS | 2022 | [PUB] [CODE] |
| Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks | Columbia University | NeurIPS | 2022 | [PUB] [PDF] |
| Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective | PKU | NeurIPS | 2022 | [PUB] |
| Subspace Recovery from Heterogeneous Data with Non-isotropic Noise | Stanford | NeurIPS | 2022 | [PUB] [PDF] |
| EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization | KAUST | NeurIPS | 2022 | [PUB] [PDF] |
| On-Demand Sampling: Learning Optimally from Multiple Distributions | UC Berkeley | NeurIPS | 2022 | [PUB] [CODE] |
| Improved Utility Analysis of Private CountSketch | itu | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning | Huawei | NeurIPS | 2022 | [PUB] [CODE] |
| Decentralized Local Stochastic Extra-Gradient for Variational Inequalities | phystech | NeurIPS | 2022 | [PUB] [PDF] |
| BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression | Princeton | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning | The University of Tokyo | NeurIPS | 2022 | [PUB] [PDF] |
| Near-Optimal Collaborative Learning in Bandits | INRIA; Inserm | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees | phystech | NeurIPS | 2022 | [PUB] [PDF] |
| Towards Optimal Communication Complexity in Distributed Non-Convex Optimization | TTIC | NeurIPS | 2022 | [PUB] [CODE] |
| FedPop: A Bayesian Approach for Personalised Federated Learning | Skoltech | NeurIPS | 2022 | [PUB] [PDF] |
| Fairness in Federated Learning via Core-Stability | UIUC | NeurIPS | 2022 | [PUB] [CODE] |
| SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning | Sorbonne Université | NeurIPS | 2022 | [PUB] [PDF] |
| FedRolex: Model-Heterogeneous Federated Learning with Rolling Submodel Extraction | MSU | NeurIPS | 2022 | [PUB] [CODE] |
| On Sample Optimality in Personalized Collaborative and Federated Learning | INRIA | NeurIPS | 2022 | [PUB] |
| DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing | HKUST | NeurIPS | 2022 | [PUB] [PDF] |
| FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning | KAMIS | NeurIPS | 2022 | [PUB] |
| Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning | KAUST | NeurIPS | 2022 | [PUB] [PDF] |
| VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely? | WHU | NeurIPS | 2022 | [PUB] [CODE] |
| DENSE: Data-Free One-Shot Federated Learning | ZJU | NeurIPS | 2022 | [PUB] [PDF] |
| CalFAT: Calibrated Federated Adversarial Training with Label Skewness | ZJU | NeurIPS | 2022 | [PUB] [PDF] |
| SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max Learning | OSU | NeurIPS | 2022 | [PUB] [PDF] |
| Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning | OSU | NeurIPS | 2022 | [PUB] [PDF] |
| Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness | PKU | NeurIPS | 2022 | [PUB] |
| Federated Submodel Optimization for Hot and Cold Data Features | SJTU | NeurIPS | 2022 | [PUB] |
| BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels | UC Berkeley | NeurIPS | 2022 | [PUB] [PDF] |
| Byzantine-tolerant federated Gaussian process regression for streaming data | PSU | NeurIPS | 2022 | [PUB] [CODE] |
| SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression | CMU | NeurIPS | 2022 | [PUB] [PDF] |
| Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering | Yale | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| Communication Efficient Federated Learning for Generalized Linear Bandits | University of Virginia | NeurIPS | 2022 | [PUB] [CODE] |
| Recovering Private Text in Federated Learning of Language Models | Princeton | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning from Pre-Trained Models: A Contrastive Learning Approach | UTS | NeurIPS | 2022 | [PUB] [PDF] |
| Global Convergence of Federated Learning for Mixed Regression | Northeastern University | NeurIPS | 2022 | [PUB] [PDF] |
| Resource-Adaptive Federated Learning with All-In-One Neural Composition | JHU | NeurIPS | 2022 | [PUB] |
| Self-Aware Personalized Federated Learning | Amazon | NeurIPS | 2022 | [PUB] [PDF] |
| A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning | Northeastern University | NeurIPS | 2022 | [PUB] [PDF] |
| An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects | NUS | NeurIPS | 2022 | [PUB] |
| Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning | EPFL | NeurIPS | 2022 | [PUB] [PDF] |
| Personalized Online Federated Multi-Kernel Learning | UCI | NeurIPS | 2022 | [PUB] |
| SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training | Duke University | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| A Unified Analysis of Federated Learning with Arbitrary Client Participation | IBM | NeurIPS | 2022 | [PUB] [PDF] |
| Preservation of the Global Knowledge by Not-True Distillation in Federated Learning | KAIST | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| FedSR: A Simple and Effective Domain Generalization Method for Federated Learning | University of Oxford | NeurIPS | 2022 | [PUB] [CODE] |
| Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching | KAIST | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits | universitas | NeurIPS | 2022 | [PUB] [PDF] |
| Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework | Tulane University | NeurIPS | 2022 | [PUB] |
| On Privacy and Personalization in Cross-Silo Federated Learning | CMU | NeurIPS | 2022 | [PUB] [PDF] |
| A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning | NUS | NeurIPS | 2022 | [PUB] [PDF] [CODE] |
| FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings | Owkin | NeurIPS Datasets and Benchmarks | 2022 | [PUB] [CODE] |
| A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources | University of Pittsburgh | ICML | 2022 | [PUB] [PDF] [CODE] |
| Fast Composite Optimization and Statistical Recovery in Federated Learning | SJTU | ICML | 2022 | [PUB] [PDF] [CODE] |
| Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning | NYU | ICML | 2022 | [PUB] [PDF] [CODE] |
| The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning | Stanford; Google Research | ICML | 2022 | [PUB] [PDF] [CODE] [SLIDE] |
| The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation | Stanford; Google Research | ICML | 2022 | [PUB] [PDF] [CODE] |
| DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training | USTC | ICML | 2022 | [PUB] [PDF] [CODE] |
| FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning | University of Oulu | ICML | 2022 | [PUB] [PDF] [CODE] |
| DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning | University of Cambridge | ICML | 2022 | [PUB] [PDF] [SLIDE] [CODE] |
| Accelerated Federated Learning with Decoupled Adaptive Optimization | Universitas Auburn | ICML | 2022 | [PUB] [PDF] |
| Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling | Georgia Tech | ICML | 2022 | [PUB] [PDF] |
| Multi-Level Branched Regularization for Federated Learning | Seoul National University | ICML | 2022 | [PUB] [PDF] [CODE] [PAGE] |
| FedScale: Benchmarking Model and System Performance of Federated Learning at Scale | University of Michigan | ICML | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning with Positive and Unlabeled Data | XJTU | ICML | 2022 | [PUB] [PDF] [CODE] |
| Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning | SJTU | ICML | 2022 | [PUB] [CODE] |
| Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering | University of Michigan | ICML | 2022 | [PUB] [PDF] [CODE] |
| Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring | USTC | ICML | 2022 | [PUB] [PDF] [CODE] [SLIDE] [解读] |
| Architecture Agnostic Federated Learning for Neural Networks | The University of Texas at Austin | ICML | 2022 | [PUB] [PDF] [SLIDE] |
| Personalized Federated Learning through Local Memorization | Inria | ICML | 2022 | [PUB] [PDF] [CODE] |
| Proximal and Federated Random Reshuffling | KAUST | ICML | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning with Partial Model Personalization | Universitas Washington | ICML | 2022 | [PUB] [PDF] [CODE] |
| Generalized Federated Learning via Sharpness Aware Minimization | University of South Florida | ICML | 2022 | [PUB] [PDF] |
| FedNL: Making Newton-Type Methods Applicable to Federated Learning | KAUST | ICML | 2022 | [PUB] [PDF] [VIDEO] [SLIDE] |
| Federated Minimax Optimization: Improved Convergence Analyses and Algorithms | CMU | ICML | 2022 | [PUB] [PDF] [SLIDE] |
| Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning | Hong Kong Baptist University | ICML | 2022 | [PUB] [PDF] [CODE] [解读] |
| FedNest: Federated Bilevel, Minimax, and Compositional Optimization | University of Michigan | ICML | 2022 | [PUB] [PDF] [CODE] |
| EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning | VMware Research | ICML | 2022 | [PUB] [PDF] [CODE] |
| Communication-Efficient Adaptive Federated Learning | Pennsylvania State University | ICML | 2022 | [PUB] [PDF] |
| ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training | CISPA Helmholz Center for Information Security | ICML | 2022 | [PUB] [PDF] [SLIDE] [CODE] |
| Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification | University of Maryland | ICML | 2022 | [PUB] [PDF] [CODE] |
| Anarchic Federated Learning | The Ohio State University | ICML | 2022 | [PUB] [PDF] |
| QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning | Nankai University | ICML | 2022 | [PUB] [CODE] |
| Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization | KAIST | ICML | 2022 | [PUB] [PDF] |
| Neural Tangent Kernel Empowered Federated Learning | NC State University | ICML | 2022 | [PUB] [PDF] [CODE] |
| Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy | UMN | ICML | 2022 | [PUB] [PDF] |
| Personalized Federated Learning via Variational Bayesian Inference | KAS | ICML | 2022 | [PUB] [PDF] [SLIDE] [UC.] |
| Federated Learning with Label Distribution Skew via Logits Calibration | ZJU | ICML | 2022 | [PUB] |
| Neurotoxin: Durable Backdoors in Federated Learning | Southeast University;Princeton | ICML | 2022 | [PUB] [PDF] [CODE] |
| Resilient and Communication Efficient Learning for Heterogeneous Federated Systems | Michigan State University | ICML | 2022 | [PUB] |
| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST | ICLR (oral) | 2022 | [PUB] [CODE] |
| Bayesian Framework for Gradient Leakage | ETH Zurich | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning from only unlabeled data with class-conditional-sharing clients | The University of Tokyo; CUHK | ICLR | 2022 | [PUB] [CODE] |
| FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning | CMU; University of Illinois at Urbana-Champaign; Universitas Washington | ICLR | 2022 | [PUB] [PDF] |
| Acceleration of Federated Learning with Alleviated Forgetting in Local Training | KAMIS | ICLR | 2022 | [PUB] [PDF] [CODE] |
| FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated Learning | POSTECH | ICLR | 2022 | [PUB] [PDF] [CODE] |
| An Agnostic Approach to Federated Learning with Class Imbalance | University of Pennsylvania | ICLR | 2022 | [PUB] [CODE] |
| Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization | Michigan State University; The University of Texas at Austin | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models | University of Maryland; NYU | ICLR | 2022 | [PUB] [PDF] [CODE] |
| ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | University of Cambridge; University of Oxford | ICLR | 2022 | [PUB] [PDF] |
| Diverse Client Selection for Federated Learning via Submodular Maximization | Intel; CMU | ICLR | 2022 | [PUB] [CODE] |
| Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? | Purdue | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions | University of Maryland; Google | ICLR | 2022 | [PUB] [CODE] |
| Towards Model Agnostic Federated Learning Using Knowledge Distillation | EPFL | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Divergence-aware Federated Self-Supervised Learning | NTU; SenseTime | ICLR | 2022 | [PUB] [PDF] [CODE] |
| What Do We Mean by Generalization in Federated Learning? | Stanford; Google | ICLR | 2022 | [PUB] [PDF] [CODE] |
| FedBABU: Toward Enhanced Representation for Federated Image Classification | KAIST | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing | EPFL | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters | Aibee | ICLR Spotlight | 2022 | [PUB] [PDF] [PAGE] [解读] |
| Hybrid Local SGD for Federated Learning with Heterogeneous Communications | University of Texas; Pennsylvania State University | ICLR | 2022 | [PUB] |
| On Bridging Generic and Personalized Federated Learning for Image Classification | The Ohio State University | ICLR | 2022 | [PUB] [PDF] [CODE] |
| Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | KAIST; MIT | ICLR | 2022 | [PUB] [PDF] |
| One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them. | JMLR | 2021 | [PUB] [CODE] | |
| Constrained differentially private federated learning for low-bandwidth devices | UAI | 2021 | [PUB] [PDF] | |
| Federated stochastic gradient Langevin dynamics | UAI | 2021 | [PUB] [PDF] | |
| Federated Learning Based on Dynamic Regularization | BU; LENGAN | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning | The Ohio State University | ICLR | 2021 | [PUB] [PDF] |
| HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | Duke University | ICLR | 2021 | [PUB] [PDF] [CODE] |
| FedMix: Approximation of Mixup under Mean Augmented Federated Learning | KAIST | ICLR | 2021 | [PUB] [PDF] |
| Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms | CMU; Google | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Adaptive Federated Optimization | ICLR | 2021 | [PUB] [PDF] [CODE] | |
| Personalized Federated Learning with First Order Model Optimization | Stanford; NVIDIA | ICLR | 2021 | [PUB] [PDF] [CODE] [UC.] |
| FedBN: Federated Learning on Non-IID Features via Local Batch Normalization | Princeton | ICLR | 2021 | [PUB] [PDF] [CODE] |
| FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning | The Ohio State University | ICLR | 2021 | [PUB] [PDF] [CODE] |
| Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning | KAIST | ICLR | 2021 | [PUB] [PDF] [CODE] |
| KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation | ZJU | ICML | 2021 | [PUB] [PDF] [CODE] [解读] |
| Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix | Harvard University | ICML | 2021 | [PUB] [PDF] [VIDEO] [CODE] |
| FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis | PKU; Princeton | ICML | 2021 | [PUB] [PDF] [VIDEO] |
| Personalized Federated Learning using Hypernetworks | Bar-Ilan University; NVIDIA | ICML | 2021 | [PUB] [PDF] [CODE] [PAGE] [VIDEO] [解读] |
| Federated Composite Optimization | Stanford; Google | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| Exploiting Shared Representations for Personalized Federated Learning | University of Texas at Austin; University of Pennsylvania | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Data-Free Knowledge Distillation for Heterogeneous Federated Learning | Michigan State University | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Federated Continual Learning with Weighted Inter-client Transfer | KAIST | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity | The University of Iowa | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning | The University of Tokyo | ICML | 2021 | [PUB] [PDF] [VIDEO] |
| Federated Learning of User Verification Models Without Sharing Embeddings | Qualcomm | ICML | 2021 | [PUB] [PDF] [VIDEO] |
| Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning | Accenture | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Ditto: Fair and Robust Federated Learning Through Personalization | CMU; Facebook AI | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Heterogeneity for the Win: One-Shot Federated Clustering | CMU | ICML | 2021 | [PUB] [PDF] [VIDEO] |
| The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] | |
| Debiasing Model Updates for Improving Personalized Federated Training | BU; Lengan | ICML | 2021 | [PUB] [CODE] [VIDEO] |
| One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning | Toyota; Berkeley; Cornell University | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| CRFL: Certifiably Robust Federated Learning against Backdoor Attacks | UIUC; IBM | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Federated Learning under Arbitrary Communication Patterns | Indiana University; Amazon | ICML | 2021 | [PUB] [VIDEO] |
| CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression | CMU | NeurIPS | 2021 | [PUB] [PDF] |
| Boosting with Multiple Sources | NeurIPS | 2021 | [PUB] | |
| DRIVE: One-bit Distributed Mean Estimation | VMware | NeurIPS | 2021 | [PUB] [CODE] |
| Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning | NUS | NeurIPS | 2021 | [PUB] [CODE] |
| Gradient Inversion with Generative Image Prior | POSTECH | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Distributed Machine Learning with Sparse Heterogeneous Data | University of Oxford | NeurIPS | 2021 | [PUB] [PDF] |
| Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning | Universitas California | NeurIPS | 2021 | [PUB] [PDF] |
| Sageflow: Robust Federated Learning against Both Stragglers and Adversaries | KAIST | NeurIPS | 2021 | [PUB] |
| CAFE: Catastrophic Data Leakage in Vertical Federated Learning | Rensselaer Polytechnic Institute; IBM Research | NeurIPS | 2021 | [PUB] [CODE] |
| Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee | NUS | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Optimality and Stability in Federated Learning: A Game-theoretic Approach | Cornell University | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning | Universitas California | NeurIPS | 2021 | [PUB] [PDF] [CODE] [解读] |
| The Skellam Mechanism for Differentially Private Federated Learning | Google Research; CMU | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data | NUS; Huawei | NeurIPS | 2021 | [PUB] [PDF] |
| STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning | UMN | NeurIPS | 2021 | [PUB] [PDF] |
| Subgraph Federated Learning with Missing Neighbor Generation | Emory; UBC; Lehigh University | NeurIPS | 2021 | [PUB] [PDF] [CODE] [解读] |
| Evaluating Gradient Inversion Attacks and Defenses in Federated Learning | Princeton | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Personalized Federated Learning With Gaussian Processes | Bar-Ilan University | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Differentially Private Federated Bayesian Optimization with Distributed Exploration | MIT; NUS | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Parameterized Knowledge Transfer for Personalized Federated Learning | PolyU | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Reconstruction: Partially Local Federated Learning | Google Research | NeurIPS | 2021 | [PUB] [PDF] [CODE] [UC.] |
| Fast Federated Learning in the Presence of Arbitrary Device Unavailability | THU; Princeton; MIT | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective | Duke University; Accenture Labs | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout | KAUST; Samsung AI Center | NeurIPS | 2021 | [PUB] [PDF] |
| Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients | University of Pennsylvania | NeurIPS | 2021 | [PUB] [PDF] [VIDEO] |
| Federated Multi-Task Learning under a Mixture of Distributions | INRIA; Accenture Labs | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Graph Classification over Non-IID Graphs | emosi | NeurIPS | 2021 | [PUB] [PDF] [CODE] [解读] |
| Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing | CMU; Hewlett Packard Enterprise | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| On Large-Cohort Training for Federated Learning | Google; CMU | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning | KAUST; Columbia University; University of Central Florida | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization | Huawei | NeurIPS | 2021 | [PUB] [VIDEO] |
| Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis | KAIST | NeurIPS | 2021 | [PUB] [PDF] |
| Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning | THU; Alibaba; Weill Cornell Medicine | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Linear Contextual Bandits | The Pennsylvania State University; Facebook; University of Virginia | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Few-Round Learning for Federated Learning | KAIST | NeurIPS | 2021 | [PUB] |
| Breaking the centralized barrier for cross-device federated learning | EPFL; Google Research | NeurIPS | 2021 | [PUB] [CODE] [VIDEO] |
| Federated-EM with heterogeneity mitigation and variance reduction | Ecole Polytechnique; Google Research | NeurIPS | 2021 | [PUB] [PDF] |
| Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning | MIT; Amazon; Google | NeurIPS | 2021 | [PUB] [PAGE] [SLIDE] |
| FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization | University of North Carolina at Chapel Hill; IBM Research | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
| Federated Adversarial Domain Adaptation | BU; Columbia University; Rutgers University | ICLR | 2020 | [PUB] [PDF] [CODE] |
| DBA: Distributed Backdoor Attacks against Federated Learning | ZJU; IBM Research | ICLR | 2020 | [PUB] [CODE] |
| Fair Resource Allocation in Federated Learning | CMU; Facebook AI | ICLR | 2020 | [PUB] [PDF] [CODE] |
| Federated Learning with Matched Averaging | University of Wisconsin-Madison; IBM Research | ICLR | 2020 | [PUB] [PDF] [CODE] |
| Differentially Private Meta-Learning | CMU | ICLR | 2020 | [PUB] [PDF] |
| Generative Models for Effective ML on Private, Decentralized Datasets | ICLR | 2020 | [PUB] [PDF] [CODE] | |
| On the Convergence of FedAvg on Non-IID Data | PKU | ICLR | 2020 | [PUB] [PDF] [CODE] [解读] |
| FedBoost: A Communication-Efficient Algorithm for Federated Learning | ICML | 2020 | [PUB] [VIDEO] | |
| FetchSGD: Communication-Efficient Federated Learning with Sketching | UC Berkeley; Johns Hopkins University; Amazon | ICML | 2020 | [PUB] [PDF] [VIDEO] [CODE] |
| SCAFFOLD: Stochastic Controlled Averaging for Federated Learning | EPFL; Google | ICML | 2020 | [PUB] [PDF] [VIDEO] [UC.] [解读] |
| Federated Learning with Only Positive Labels | ICML | 2020 | [PUB] [PDF] [VIDEO] | |
| From Local SGD to Local Fixed-Point Methods for Federated Learning | Moscow Institute of Physics and Technology; KAUST | ICML | 2020 | [PUB] [PDF] [SLIDE] [VIDEO] |
| Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization | KAUST | ICML | 2020 | [PUB] [PDF] [SLIDE] [VIDEO] |
| Differentially-Private Federated Linear Bandits | MIT | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Federated Principal Component Analysis | University of Cambridge; Quine Technologies | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| FedSplit: an algorithmic framework for fast federated optimization | UC Berkeley | NeurIPS | 2020 | [PUB] [PDF] |
| Federated Bayesian Optimization via Thompson Sampling | NUS; MIT | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Lower Bounds and Optimal Algorithms for Personalized Federated Learning | KAUST | NeurIPS | 2020 | [PUB] [PDF] |
| Robust Federated Learning: The Case of Affine Distribution Shifts | UC Santa Barbara; MIT | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| An Efficient Framework for Clustered Federated Learning | UC Berkeley; DeepMind | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Distributionally Robust Federated Averaging | Pennsylvania State University | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Personalized Federated Learning with Moreau Envelopes | The University of Sydney | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach | MIT; UT Austin | NeurIPS | 2020 | [PUB] [PDF] [UC.] |
| Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge | USC | NeurIPS | 2020 | [PUB] [PDF] [CODE] [解读] |
| Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization | CMU; Princeton | NeurIPS | 2020 | [PUB] [PDF] [CODE] [UC.] |
| Attack of the Tails: Yes, You Really Can Backdoor Federated Learning | University of Wisconsin-Madison | NeurIPS | 2020 | [PUB] [PDF] |
| Federated Accelerated Stochastic Gradient Descent | Stanford | NeurIPS | 2020 | [PUB] [PDF] [CODE] [VIDEO] |
| Inverting Gradients - How easy is it to break privacy in federated learning? | University of Siegen | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Ensemble Distillation for Robust Model Fusion in Federated Learning | EPFL | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Throughput-Optimal Topology Design for Cross-Silo Federated Learning | INRIA | NeurIPS | 2020 | [PUB] [PDF] [CODE] |
| Bayesian Nonparametric Federated Learning of Neural Networks | IBM | ICML | 2019 | [PUB] [PDF] [CODE] |
| Analyzing Federated Learning through an Adversarial Lens | Princeton; IBM | ICML | 2019 | [PUB] [PDF] [CODE] |
| Agnostic Federated Learning | ICML | 2019 | [PUB] [PDF] | |
| cpSGD: Communication-efficient and differentially-private distributed SGD | Princeton; Google | NeurIPS | 2018 | [PUB] [PDF] |
| Federated Multi-Task Learning | Stanford; USC; CMU | NeurIPS | 2017 | [PUB] [PDF] [CODE] |
Federated Learning papers accepted by top DM(Data Mining) conference and journal, Including KDD(ACM SIGKDD Conference on Knowledge Discovery and Data Mining) and WSDM(Web Search and Data Mining).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics | KDD Workshop | 2024 | [PUB] | |
| Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination | KDD | 2024 | [PUB] | |
| BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning | KDD | 2024 | [PUB] | |
| Federated Graph Learning with Structure Proxy Alignment | KDD | 2024 | [PUB] | |
| HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning | KDD | 2024 | [PUB] | |
| FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs | KDD | 2024 | [PUB] | |
| Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization | KDD | 2024 | [PUB] | |
| FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning | KDD | 2024 | [PUB] | |
| On the Convergence of Zeroth-Order Federated Tuning for Large Language Models | KDD | 2024 | [PUB] | |
| CASA: Clustered Federated Learning with Asynchronous Clients | KDD | 2024 | [PUB] | |
| FLAIM: AIM-based Synthetic Data Generation in the Federated Setting | KDD | 2024 | [PUB] | |
| Privacy-Preserving Federated Learning using Flower Framework | KDD | 2024 | [PUB] | |
| FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning | KDD | 2024 | [PUB] | |
| FedNLR: Federated Learning with Neuron-wise Learning Rates | KDD | 2024 | [PUB] | |
| FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model | KDD | 2024 | [PUB] | |
| FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation | KDD | 2024 | [PUB] | |
| Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning | KDD | 2024 | [PUB] | |
| PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection | KDD | 2024 | [PUB] | |
| FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation | KDD | 2024 | [PUB] | |
| FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction | KDD | 2024 | [PUB] | |
| OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning | KDD | 2024 | [PUB] | |
| Personalized Federated Continual Learning via Multi-Granularity Prompt | KDD | 2024 | [PUB] | |
| Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning | KDD | 2024 | [PUB] | |
| GPFedRec: Graph-Guided Personalization for Federated Recommendation | KDD | 2024 | [PUB] | |
| Asynchronous Vertical Federated Learning for Kernelized AUC Maximization | KDD | 2024 | [PUB] | |
| VertiMRF: Differentially Private Vertical Federated Data Synthesis | KDD | 2024 | [PUB] | |
| User Consented Federated Recommender System Against Personalized Attribute Inference Attack | HKUST | WSDM | 2024 | [PUB] [PDF] [CODE] |
| Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning | ECNU | WSDM | 2024 | [PUB] |
| Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation | University of Cambridge | KDD | 2023 | [PUB] [PDF] |
| FedDefender: Client-Side Attack-Tolerant Federated Learning | KAIST | KDD | 2023 | [PUB] [PDF] [CODE] |
| FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity | ZJU | KDD | 2023 | [PUB] [CODE] |
| FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis | UMBC | KDD | 2023 | [PUB] [PDF] |
| ShapleyFL: Robust Federated Learning Based on Shapley Value | ZJU | KDD | 2023 | [PUB] [CODE] |
| Federated Few-shot Learning | University of Virginia | KDD | 2023 | [PUB] [PDF] [CODE] |
| Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity | SDU | KDD | 2023 | [PUB] |
| Personalized Federated Learning with Parameter Propagation | UIUC | KDD | 2023 | [PUB] |
| Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining | University of Pittsburgh | KDD | 2023 | [PUB] [PDF] [CODE] |
| CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning | SUNY-Binghamton University | KDD | 2023 | [PUB] [PDF] |
| FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework | L3S Research Center | KDD | 2023 | [PUB] [PDF] |
| FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy | SJTU | KDD | 2023 | [PUB] [PDF] [CODE] |
| Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework | UCSD | KDD | 2023 | [PUB] [PDF] [CODE] |
| DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization | BUAA | KDD | 2023 | [PUB] [CODE] |
| FS-REAL: Towards Real-World Cross-Device Federated Learning | Grup Alibaba | KDD | 2023 | [PUB] [PDF] |
| FedMultimodal: A Benchmark for Multimodal Federated Learning | USC | KDD | 2023 | [PUB] [PDF] [CODE] |
| PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation | RUC | KDD | 2023 | [PUB] [PDF] [NEWS] |
| Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks | HKUST; Grup Alibaba | KDD | 2023 | [PUB] [PDF] [CODE] |
| UA-FedRec: Untargeted Attack on Federated News Recommendation | USTC | KDD | 2023 | [PUB] [PDF] [CODE] |
| International Workshop on Federated Learning for Distributed Data Mining | MSU | KDD Workshop Summaries | 2023 | [PUB] [PAGE] |
| Is Normalization Indispensable for Multi-domain Federated Learning? | KDD workshop | 2023 | [PUB] | |
| Distributed Personalized Empirical Risk Minimization. | KDD workshop | 2023 | [PUB] | |
| Once-for-All Federated Learning: Learning From and Deploying to Heterogeneous Clients. | KDD workshop | 2023 | [PUB] | |
| SparseVFL: Communication-Efficient Vertical Federated Learning Based on Sparsification of Embeddings and Gradients. | KDD workshop | 2023 | [PUB] | |
| Optimization of User Resources in Federated Learning for Urban Sensing Applications | KDD workshop | 2023 | [PUB] | |
| FedLEGO: Enabling Heterogenous Model Cooperation via Brick Reassembly in Federated Learning. | KDD workshop | 2023 | [PUB] | |
| Federated Graph Analytics with Differential Privacy. | KDD workshop | 2023 | [PUB] | |
| Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. | KDD workshop | 2023 | [PUB] | |
| Uncertainty Quantification in Federated Learning for Heterogeneous Health Data | KDD workshop | 2023 | [PUB] | |
| A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing. | KDD workshop | 2023 | [PUB] | |
| Taming Heterogeneity to Deal with Test-Time Shift in Federated Learning. | KDD workshop | 2023 | [PUB] | |
| Federated Blood Supply Chain Demand Forecasting: A Case Study. | KDD workshop | 2023 | [PUB] | |
| Stochastic Clustered Federated Learning. | KDD workshop | 2023 | [PUB] | |
| A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection. | KDD workshop | 2023 | [PUB] | |
| Exploring the Efficacy of Data-Decoupled Federated Learning for Image Classification and Medical Imaging Analysis. | KDD workshop | 2023 | [PUB] | |
| FedNoisy: A Federated Noisy Label Learning Benchmark | KDD workshop | 2023 | [PUB] | |
| Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging | KDD workshop | 2023 | [PUB] | |
| Federated learning for competing risk analysis in healthcare. | KDD workshop | 2023 | [PUB] | |
| Federated Threat Detection for Smart Home IoT rules. | KDD workshop | 2023 | [PUB] | |
| Federated Unlearning for On-Device Recommendation | UQ | WSDM | 2023 | [PUB] [PDF] |
| Collaboration Equilibrium in Federated Learning | KAMIS | KDD | 2022 | [PUB] [PDF] [CODE] |
| Connected Low-Loss Subspace Learning for a Personalization in Federated Learning | Ulsan National Institute of Science and Technology | KDD | 2022 | [PUB] [PDF] [CODE] |
| FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks | University of Virginia | KDD | 2022 | [PUB] |
| Communication-Efficient Robust Federated Learning with Noisy Labels | University of Pittsburgh | KDD | 2022 | [PUB] [PDF] |
| FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency | USTC | KDD | 2022 | [PUB] [PDF] [CODE] |
| Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data | HKUST | KDD | 2022 | [PUB] [PDF] [CODE] |
| FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy | SJTU | KDD | 2022 | [PUB] [PDF] |
| FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning | Alibaba | KDD (Best Paper Award) | 2022 | [PUB] [PDF] [CODE] |
| Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch | BUAA | KDD | 2022 | [PUB] [PDF] [解读] |
| Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks | USTC | KDD | 2022 | [PUB] [PDF] |
| No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices | Renmin University of China | KDD | 2022 | [PUB] [PDF] |
| FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling | KAMIS | KDD | 2022 | [PUB] [PDF] [CODE] |
| PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion | The University of Queensland | WSDM | 2022 | [PUB] [PDF] |
| Fed2: Feature-Aligned Federated Learning | George Mason University; Microsoft; University of Maryland | KDD | 2021 | [PUB] [PDF] |
| FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data | Nanjing University | KDD | 2021 | [PUB] [CODE] |
| Federated Adversarial Debiasing for Fair and Trasnferable Representations | Michigan State University | KDD | 2021 | [PUB] [PAGE] [CODE] [SLIDE] |
| Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling | USC | KDD | 2021 | [PUB] [CODE] [解读] |
| AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization | Xidian University;JD Tech | KDD | 2021 | [PUB] [PDF] |
| FLOP: Federated Learning on Medical Datasets using Partial Networks | Duke University | KDD | 2021 | [PUB] [PDF] [CODE] |
| A Practical Federated Learning Framework for Small Number of Stakeholders | ETH Zürich | WSDM | 2021 | [PUB] [CODE] |
| Federated Deep Knowledge Tracing | USTC | WSDM | 2021 | [PUB] [CODE] |
| FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems | University College Dublin | KDD | 2020 | [PUB] [VIDEO] |
| Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data | JD Tech | KDD | 2020 | [PUB] [PDF] [VIDEO] |
| Federated Online Learning to Rank with Evolution Strategies | Facebook AI Research | WSDM | 2019 | [PUB] [CODE] |
Federated Learning papers accepted by top Secure conference and journal, Including S&P(IEEE Symposium on Security and Privacy), CCS(Conference on Computer and Communications Security), USENIX Security(Usenix Security Symposium) and NDSS(Network and Distributed System Security Symposium).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| Byzantine-Robust Decentralized Federated Learning | CCS | 2024 | [PUB] | |
| Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation | CCS | 2024 | [PUB] | |
| Cross-silo Federated Learning with Record-level Personalized Differential Privacy. | CCS | 2024 | [PUB] | |
| Samplable Anonymous Aggregation for Private Federated Data Analysis | CCS | 2024 | [PUB] | |
| Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy | CCS | 2024 | [PUB] | |
| Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses | CCS | 2024 | [PUB] | |
| Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning. | CCS | 2024 | [PUB] | |
| Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling. | CCS | 2024 | [PUB] | |
| Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration. | CCS | 2024 | [PUB] | |
| FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting | NDSS | 2024 | [PUB] | |
| FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning | NDSS | 2024 | [PUB] | |
| Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning | NDSS | 2024 | [PUB] | |
| CrowdGuard: Federated Backdoor Detection in Federated Learning | NDSS | 2024 | [PUB] | |
| Protecting Label Distribution in Cross-Silo Federated Learning | S&P | 2024 | [PUB] | |
| FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks | S&P | 2024 | [PUB] | |
| BadVFL: Backdoor Attacks in Vertical Federated Learning | S&P | 2024 | [PUB] | |
| SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks | S&P | 2024 | [PUB] | |
| Loki: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation | S&P | 2024 | [PUB] | |
| Poster: Towards Privacy-Preserving Federated Recommendation via Synthetic Interactions. | S&P Workshop | 2024 | [PUB] | |
| A Performance Analysis for Confidential Federated Learning. | S&P Workshop | 2024 | [PUB] | |
| Turning Privacy-preserving Mechanisms against Federated Learning | University of Pavia | CCS | 2023 | [PUB] [PDF] |
| MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers | University of Würzburg | CCS | 2023 | [PUB] |
| martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture | KAMIS | CCS | 2023 | [PUB] [PDF] [CODE] |
| Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks | UIUC | CCS | 2023 | [PUB] [PDF] |
| Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning | NSYSU | CCS | 2023 | [PUB] |
| Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems | RWTH Aachen University | CCS | 2023 | [PUB] |
| Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks | University of Massachusetts Amherst | USENIX Security | 2023 | [PUB] [PDF] |
| PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation | JHU | USENIX Security | 2023 | [PUB] [CODE] |
| Gradient Obfuscation Gives a False Sense of Security in Federated Learning | NCSU | USENIX Security | 2023 | [PUB] [PDF] [CODE] |
| FedVal: Different good or different bad in federated learning | AI Sweden | USENIX Security | 2023 | [PUB] [PDF] [CODE] |
| Securing Federated Sensitive Topic Classification against Poisoning Attacks | IMDEA Networks Institute | NDSS | 2023 | [PUB] [PDF] [CODE] |
| PPA: Preference Profiling Attack Against Federated Learning | NJUST | NDSS | 2023 | [PUB] [PDF] |
| Turning Privacy-preserving Mechanisms against Federated Learning | University of Pavia; TU Delft; University of Padua; Radboud University | CCS | 2023 | [PUB] [PDF] [CODE] |
| CERBERUS: Exploring Federated Prediction of Security Events | UCL London | CCS | 2022 | [PUB] [PDF] |
| EIFFeL: Ensuring Integrity for Federated Learning | UW-Madison | CCS | 2022 | [PUB] [PDF] |
| Eluding Secure Aggregation in Federated Learning via Model Inconsistency | SPRING Lab; EPFL | CCS | 2022 | [PUB] [PDF] [CODE] |
| Federated Boosted Decision Trees with Differential Privacy | University of Warwick | CCS | 2022 | [PUB] [PDF] [CODE] |
| FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information | Duke University | S&P | 2023 | [PUB] [PDF] |
| Scalable and Privacy-Preserving Federated Principal Component Analysis | EPFL; Tune Insight SA | S&P | 2023 | [PUB] [PDF] |
| SafeFL: MPC-friendly Framework for Private and Robust Federated Learning | TU Darmstadt | S&P Workshop | 2023 | [PUB] |
| On the Pitfalls of Security Evaluation of Robust Federated Learning. | umass | S&P Workshop | 2023 | [PUB] |
| BayBFed: Bayesian Backdoor Defense for Federated Learning | TU Darmstadt; UTSA | S&P | 2023 | [PUB] [PDF] |
| 3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning | PolyU | S&P | 2023 | [PUB] [CODE] |
| RoFL: Robustness of Secure Federated Learning. | ETH Zurich | S&P | 2023 | [PUB] [PDF] [CODE] |
| Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning. | upenn | S&P | 2023 | [PUB] [CODE] |
| ELSA: Secure Aggregation for Federated Learning with Malicious Actors. | S&P | 2023 | ||
| Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy | Fudan University | S&P | 2023 | [PUB] [PDF] |
| Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning | University of Massachusetts | S&P | 2022 | [PUB] [VIDEO] |
| SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost | Microsoft Research | USENIX Security | 2022 | [PUB] [PDF] [CODE] [VIDEO] [SUPP] |
| Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors | University of Vermont | USENIX Security | 2022 | [PUB] [SLIDE] [VIDEO] |
| Label Inference Attacks Against Vertical Federated Learning | ZJU | USENIX Security | 2022 | [PUB] [SLIDE] [CODE] [VIDEO] |
| FLAME: Taming Backdoors in Federated Learning | Technical University of Darmstadt | USENIX Security | 2022 | [PUB] [SLIDE] [PDF] [VIDEO] |
| Local and Central Differential Privacy for Robustness and Privacy in Federated Learning | University at Buffalo, SUNY | NDSS | 2022 | [PUB] [PDF] [VIDEO] [UC.] |
| Interpretable Federated Transformer Log Learning for Cloud Threat Forensics | University of the Incarnate Word | NDSS | 2022 | [PUB] [VIDEO] [UC.] |
| FedCRI: Federated Mobile Cyber-Risk Intelligence | Technical University of Darmstadt | NDSS | 2022 | [PUB] [VIDEO] |
| DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection | Technical University of Darmstadt | NDSS | 2022 | [PUB] [PDF] [VIDEO] |
| Private Hierarchical Clustering in Federated Networks | NUS | CCS | 2021 | [PUB] [PDF] |
| FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping | Duke University | NDSS | 2021 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| POSEIDON: Privacy-Preserving Federated Neural Network Learning | EPFL | NDSS | 2021 | [PUB] [VIDEO] |
| Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning | University of Massachusetts Amherst | NDSS | 2021 | [PUB] [CODE] [VIDEO] |
| SAFELearn: Secure Aggregation for private FEderated Learning | TU Darmstadt | S&P Workshop | 2021 | [PUB] |
| Local Model Poisoning Attacks to Byzantine-Robust Federated Learning | The Ohio State University | USENIX Security | 2020 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
| A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain | Universitas Kansas | CCS (Poster) | 2019 | [PUB] |
| IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning | Université du Québéc á Montréal | S&P Workshop | 2019 | [PUB] |
| Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning | University of Massachusetts Amherst | S&P | 2019 | [PUB] [VIDEO] [SLIDE] [CODE] |
| Practical Secure Aggregation for Privacy Preserving Machine Learning | CCS | 2017 | [PUB] [PDF] [解读] [UC.] [UC] |
Federated Learning papers accepted by top CV(computer vision) conference and journal, Including CVPR(Computer Vision and Pattern Recognition), ICCV(IEEE International Conference on Computer Vision), ECCV(European Conference on Computer Vision), MM(ACM International Conference on Multimedia), IJCV(International Journal of Computer Vision).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations | MM | 2024 | [PUB] | |
| One-shot-but-not-degraded Federated Learning | MM | 2024 | [PUB] | |
| Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning | MM | 2024 | [PUB] | |
| FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models | MM | 2024 | [PUB] | |
| Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition | MM | 2024 | [PUB] | |
| CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation | MM | 2024 | [PUB] | |
| Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training | MM | 2024 | [PUB] | |
| FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework | MM | 2024 | [PUB] | |
| One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity | MM | 2024 | [PUB] | |
| FedSLS: Exploring Federated Aggregation in Saliency Latent Space | MM | 2024 | [PUB] | |
| Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia | MM | 2024 | [PUB] | |
| FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning | MM | 2024 | [PUB] | |
| Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data | MM | 2024 | [PUB] | |
| Cross-Modal Meta Consensus for Heterogeneous Federated Learning | MM | 2024 | [PUB] | |
| Masked Random Noise for Communication-Efficient Federated Learning | MM | 2024 | [PUB] | |
| Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations | MM | 2024 | [PUB] | |
| Adaptive Hierarchical Aggregation for Federated Object Detection | MM | 2024 | [PUB] | |
| FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement | MM | 2024 | [PUB] | |
| Federated Fuzzy C-means with Schatten-p Norm Minimization | MM | 2024 | [PUB] | |
| Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation | MM | 2024 | [PUB] | |
| Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification | IJCV | 2024 | [PUB] | |
| FedHide: Federated Learning by Hiding in the Neighbors | ECCV | 2024 | [PUB] | |
| FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation | ECCV | 2024 | [PUB] | |
| FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients | ECCV | 2024 | [PUB] | |
| Pick-a-Back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning | ECCV | 2024 | [PUB] | |
| Federated Learning with Local Openset Noisy Labels | ECCV | 2024 | [PUB] | |
| FedTSA: A Cluster-Based Two-Stage Aggregation Method for Model-Heterogeneous Federated Learning. | ECCV | 2024 | [PUB] | |
| Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection | ECCV | 2024 | [PUB] | |
| BAFFLE: A Baseline of Backpropagation-Free Federated Learning | ECCV | 2024 | [PUB] | |
| PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning | ECCV | 2024 | [PUB] | |
| Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning | ECCV | 2024 | [PUB] | |
| Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents | ECCV | 2024 | [PUB] | |
| FedHARM: Harmonizing Model Architectural Diversity in Federated Learning | ECCV | 2024 | [PUB] | |
| SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. | ECCV | 2024 | [PUB] | |
| Personalized Federated Domain-Incremental Learning Based on Adaptive Knowledge Matching. | ECCV | 2024 | [PUB] | |
| Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning | ECCV | 2024 | [PUB] | |
| Towards Multi-modal Transformers in Federated Learning | ECCV | 2024 | [PUB] | |
| Local and Global Flatness for Federated Domain Generalization | ECCV | 2024 | [PUB] | |
| Feature Diversification and Adaptation for Federated Domain Generalization | ECCV | 2024 | [PUB] | |
| PFEDEDIT: Personalized Federated Learning via Automated Model Editing | ECCV | 2024 | [PUB] | |
| FedHCA2: Towards Hetero-Client Federated Multi-Task Learning | SJTU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity | WHU | CVPR | 2024 | [PUB] [PDF] [CODE] |
| Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts | NWPU; HKUST | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| FedMef: Towards Memory-efficient Federated Dynamic Pruning | CUHK | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| Communication-Efficient Federated Learning with Accelerated Client Gradient | SNU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space | IITH | CVPR | 2024 | [PUB] [SUPP] [CODE] |
| Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning | TJUT | CVPR | 2024 | [PUB] [SUPP] [CODE] |
| Towards Efficient Replay in Federated Incremental Learning | HUST | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices | UT | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| Data Valuation and Detections in Federated Learning | NUS | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Decentralized Directed Collaboration for Personalized Federated Learning | NJUST | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning | UBC | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Global and Local Prompts Cooperation via Optimal Transport for Federated Learning | ShanghaiTech University | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data | ZJU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Relaxed Contrastive Learning for Federated Learning | SNU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning | Purdue University | CVPR | 2024 | [PUB] [SUPP] [PDF] [VIDEO] |
| Traceable Federated Continual Learning | BUPT | CVPR | 2024 | [PUB] [SUPP] [CODE] |
| Federated Online Adaptation for Deep Stereo | University of Bologna | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] [PAGE] [VIDEO] |
| Federated Generalized Category Discovery | UniTn | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization | tidak | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Text-Enhanced Data-free Approach for Federated Class-Incremental Learning | Universitas Monash | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees | UIUC; NVIDIA | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning | KAIST | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| FedUV: Uniformity and Variance for Heterogeneous Federated Learning | UC Davis | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| FedAS: Bridging Inconsistency in Personalized Federated Learning | WHU | CVPR | 2024 | [PUB] [CODE] |
| FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning | Lapis Labs | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
| Device-Wise Federated Network Pruning | PITT | CVPR | 2024 | [PUB] [SUPP] |
| Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping | HNU; PolyU; AIRS | CVPR | 2024 | [PUB] [SUPP] |
| DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning | HKUST; PolyU | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning | SJTU | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] [POSTER] [SLIDES] |
| An Aggregation-Free Federated Learning for Tackling Data Heterogeneity | A* STAR | CVPR | 2024 | [PUB] [SUPP] [PDF] |
| FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning | BUAA; HKU | CVPR | 2024 | [PUB] [SUPP] [CODE] [PAGE] [POSTER] [VIDEO] |
| Collaborative Visual Place Recognition through Federated Learning | CVPR workshop | 2024 | [PUB] [SUPP] [PDF] | |
| FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer | CVPR workshop | 2024 | [PUB] [SUPP] [PDF] | |
| Federated Hyperparameter Optimization Through Reward-Based Strategies: Challenges and Insights | CVPR workshop | 2024 | [PUB] | |
| On the Efficiency of Privacy Attacks in Federated Learning | CVPR workshop | 2024 | [PUB] [PDF] | |
| FedCE: Personalized Federated Learning Method based on Clustering Ensembles | BJTU | MM | 2023 | [PUB] |
| FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes | Leiden University | MM | 2023 | [PUB] |
| Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor | XJTU | MM | 2023 | [PUB] [PDF] [CODE] |
| Federated Deep Multi-View Clustering with Global Self-Supervision | UESTC | MM | 2023 | [PUB] [PDF] |
| FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy | ZJU | MM | 2023 | [PUB] |
| Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing | SDNU | MM | 2023 | [PUB] [CODE] |
| Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data | ZJU | MM | 2023 | [PUB] [PDF] |
| FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data | BUPT | MM | 2023 | [PUB] |
| Federated Learning with Label-Masking Distillation | UCAS | MM | 2023 | [PUB] [CODE] |
| Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data | SDU | MM | 2023 | [PUB] [PDF] [CODE] |
| A Four-Pronged Defense Against Byzantine Attacks in Federated Learning | HUST | MM | 2023 | [PUB] [PDF] |
| Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning | CAS; Peng Cheng Laboratory; UCAS | MM | 2023 | [PUB] |
| FedGH: Heterogeneous Federated Learning with Generalized Global Header | NKU | MM | 2023 | [PUB] [PDF] [CODE] |
| Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation | CUHK | MM | 2023 | [PUB] [PDF] [CODE] |
| AffectFAL: Federated Active Affective Computing with Non-IID Data | TJUT | MM | 2023 | [PUB] [CODE] |
| Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation | SZU | MM | 2023 | [PUB] |
| Towards Attack-tolerant Federated Learning via Critical Parameter Analysis | KAIST | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation | NTU; NVIDIA | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning | A*STAR | ICCV | 2023 | [PUB] [CODE] [SUPP] |
| GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning | SJTU | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization | University of Houston | ICCV | 2023 | [PUB] [SUPP] |
| PGFed: Personalize Each Client's Global Objective for Federated Learning | University of Pittsburgh | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning | UCF | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning | TCL AI Lab | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| FedPD: Federated Open Set Recognition with Parameter Disentanglement | City University of Hong Kong | ICCV | 2023 | [PUB] [CODE] |
| TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation | ETH Zurich; Sony AI | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Towards Instance-adaptive Inference for Federated Learning | A*STAR | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Communication-efficient Federated Learning with Single-Step Synthetic Features Compressor for Faster Convergence | SCU; Engineering Research Center of Machine Learning and Industry Intelligence | ICCV | 2023 | [PUB] [PDF] [CODE] |
| zPROBE: Zero Peek Robustness Checks for Federated Learning | Purdue University | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| ProtoFL: Unsupervised Federated Learning via Prototypical Distillation | KakaoBank Corp. | ICCV | 2023 | [PUB] [PDF] |
| MAS: Towards Resource-Efficient Federated Multiple-Task Learning | Sony AI | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation | PKU | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| When Do Curricula Work in Federated Learning? | UCSD | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples | Duke University | ICCV | 2023 | [PUB] [PDF] [CODE] |
| Multi-Metrics Adaptively Identifies Backdoors in Federated Learning | BANGSAT | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier | ZJU | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation | Ludwig Maximilian University of Munich; Siemens Technology | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration | BUAA | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Global Balanced Experts for Federated Long-Tailed Learning | CUHK-Shenzhen | ICCV | 2023 | [PUB] [CODE] [SUPP] |
| Knowledge-Aware Federated Active Learning with Non-IID Data | The University of Sydney | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation | BUPT | ICCV | 2023 | [PUB] [SUPP] |
| Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels | CMU | ICCV | 2023 | [PUB] [PDF] [SUPP] |
| Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat | Rice University | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Robust Heterogeneous Federated Learning under Data Corruption | WHU | ICCV | 2023 | [PUB] [CODE] [SUPP] |
| Personalized Semantics Excitation for Federated Image Classification | Tulane University | ICCV | 2023 | [PUB] [CODE] |
| Reducing Training Time in Cross-Silo Federated Learning Using Multigraph Topology | AIOZ | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
| Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning. | Politecnico di Torino | ICCV workshop | 2023 | [PUB] [PDF] |
| Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning. | University of Catania | ICCV workshop | 2023 | [PUB] |
| FedRCIL: Federated Knowledge Distillation for Representation based Contrastive Incremental Learning. | Centre for Research and Technology Hellas; University of West Attica | ICCV workshop | 2023 | [PUB] [CODE] |
| FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data. | Centre for Research and Technology Hellas; University of West Attica | ICCV workshop | 2023 | [PUB] |
| Rethinking Federated Learning With Domain Shift: A Prototype View | WHU | CVPR | 2023 | [PUB] [CODE] |
| Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning | ECNU | CVPR | 2023 | [PUB] [CODE] |
| DaFKD: Domain-Aware Federated Knowledge Distillation | HUST | CVPR | 2023 | [PUB] [CODE] |
| The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning | Purdue University | CVPR | 2023 | [PUB] [PDF] |
| FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation | ZJU | CVPR | 2023 | [PUB] |
| On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data | DTU | CVPR | 2023 | [PUB] [PDF] |
| Elastic Aggregation for Federated Optimization | Meituan | CVPR | 2023 | [PUB] |
| FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning | Universitas California | CVPR | 2023 | [PUB] [PDF] |
| Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity | UM | CVPR | 2023 | [PUB] |
| ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients | GaTech | CVPR | 2023 | [PUB] [CODE] |
| Reliable and Interpretable Personalized Federated Learning | TJU | CVPR | 2023 | [PUB] |
| Federated Domain Generalization With Generalization Adjustment | SJTU | CVPR | 2023 | [PUB] [CODE] |
| Make Landscape Flatter in Differentially Private Federated Learning | KAMIS | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Confidence-Aware Personalized Federated Learning via Variational Expectation Maximization | KU Leuven | CVPR | 2023 | [PUB] [PDF] [CODE] |
| STDLens: Model Hijacking-Resilient Federated Learning for Object Detection | GaTech | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Re-Thinking Federated Active Learning Based on Inter-Class Diversity | KAIST | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Learning Federated Visual Prompt in Null Space for MRI Reconstruction | A*STAR | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Fair Federated Medical Image Segmentation via Client Contribution Estimation | CUHK | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning With Data-Agnostic Distribution Fusion | NJU | CVPR | 2023 | [PUB] [CODE] |
| How To Prevent the Poor Performance Clients for Personalized Federated Learning? | CSU | CVPR | 2023 | [PUB] |
| GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated Catastrophic Forgetting | ECNU | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Bias-Eliminating Augmentation Learning for Debiased Federated Learning | NTU | CVPR | 2023 | [PUB] |
| Federated Incremental Semantic Segmentation | CAS; UCAS | CVPR | 2023 | [PUB] [PDF] [CODE] |
| Asynchronous Federated Continual Learning | University of Padova | CVPR workshop | 2023 | [PUB] [PDF] [SILDES] [CODE] |
| Mixed Quantization Enabled Federated Learning To Tackle Gradient Inversion Attacks | UMBC | CVPR workshop | 2023 | [PUB] [CODE] |
| OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework | Meituan | CVPR workshop | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data | utexas | CVPR workshop | 2023 | [PUB] [SUPP] [PDF] [CODE] |
| TimelyFL: Heterogeneity-Aware Asynchronous Federated Learning With Adaptive Partial Training | USC | CVPR workshop | 2023 | [PUB] [PDF] |
| Many-Task Federated Learning: A New Problem Setting and a Simple Baseline | utexas | CVPR workshop | 2023 | [PUB] [CODE] |
| Confederated Learning: Going Beyond Centralization | CAS; UCAS | MM | 2022 | [PUB] |
| Few-Shot Model Agnostic Federated Learning | WHU | MM | 2022 | [PUB] [CODE] |
| Feeling Without Sharing: A Federated Video Emotion Recognition Framework Via Privacy-Agnostic Hybrid Aggregation | TJUT | MM | 2022 | [PUB] |
| FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket Networks | ECCV | 2022 | [PUB] [SUPP] | |
| Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional Medical Image Segmentation | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] | |
| Improving Generalization in Federated Learning by Seeking Flat Minima | Politecnico di Torino | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] [PAGE] | |
| SphereFed: Hyperspherical Federated Learning | ECCV | 2022 | [PUB] [SUPP] [PDF] | |
| Federated Self-Supervised Learning for Video Understanding | ECCV | 2022 | [PUB] [PDF] [CODE] | |
| FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] | |
| Addressing Heterogeneity in Federated Learning via Distributional Transformation | ECCV | 2022 | [PUB] [CODE] | |
| FedX: Unsupervised Federated Learning with Cross Knowledge Distillation | KAIST | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| Personalizing Federated Medical Image Segmentation via Local Calibration | Xiamen University | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework | MEMUKUL | CVPR | 2022 | [PUB] |
| Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning | Stanford | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] [VIDEO] |
| FedCorr: Multi-Stage Federated Learning for Label Noise Correction | Singapore University of Technology and Design | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] [VIDEO] |
| FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning | Duke University | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| Layer-Wised Model Aggregation for Personalized Federated Learning | PolyU | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning | University of Central Florida | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| Federated Learning With Position-Aware Neurons | Nanjing University | CVPR | 2022 | [PUB] [SUPP] [PDF] |
| RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning | HKUST | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| Learn From Others and Be Yourself in Heterogeneous Federated Learning | Wuhan University | CVPR | 2022 | [PUB] [CODE] [VIDEO] |
| Robust Federated Learning With Noisy and Heterogeneous Clients | Wuhan University | CVPR | 2022 | [PUB] [SUPP] [CODE] |
| ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning | Arizona State University | CVPR | 2022 | [PUB] [SUPP] [PDF] [CODE] |
| FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling and Correction | National University of Defense Technology | CVPR | 2022 | [PUB] [PDF] [CODE] [解读] |
| Federated Class-Incremental Learning | CAS; Northwestern University; UTS | CVPR | 2022 | [PUB] [PDF] [CODE] |
| Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning | PKU; JD Explore Academy; The University of Sydney | CVPR | 2022 | [PUB] [PDF] |
| Differentially Private Federated Learning With Local Regularization and Sparsification | KAS | CVPR | 2022 | [PUB] [PDF] |
| Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage | University of Tennessee; Oak Ridge National Laboratory; Google Research | CVPR | 2022 | [PUB] [PDF] [CODE] [VIDEO] |
| CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning | SJTU | CVPR | 2022 | [PUB] [PDF] |
| Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation | Univ. of Pittsburgh; NVIDIA | CVPR | 2022 | [PUB] [PDF] |
| Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning | HHI | CVPR workshop | 2022 | [PUB] [PDF] [SILDES] [VIDEO] |
| MPAF: Model Poisoning Attacks to Federated Learning Based on Fake Clients | Duke University | CVPR workshop | 2022 | [PUB] [PDF] [SILDES] [VIDEO] |
| Communication-Efficient Federated Data Augmentation on Non-IID Data | UESTC | CVPR workshop | 2022 | [PUB] |
| Does Federated Dropout Actually Work? | Stanford | CVPR workshop | 2022 | [PUB] [VIDEO] |
| FedIris: Towards More Accurate and Privacy-preserving Iris Recognition via Federated Template Communication | USTC; CRIPAC; CASIA | CVPR workshop | 2022 | [PUB] [SLIDES] [VIDEO] |
| Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic Resonance Image Reconstruction Using Federated Learning | Johns Hopkins University | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Model-Contrastive Federated Learning | NUS; UC Berkeley | CVPR | 2021 | [PUB] [PDF] [CODE] [解读] |
| FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space | CUHK | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Soteria: Provable Defense Against Privacy Leakage in Federated Learning From Representation Perspective | Duke University | CVPR | 2021 | [PUB] [PDF] [CODE] |
| Federated Learning for Non-IID Data via Unified Feature Learning and Optimization Objective Alignment | PKU | ICCV | 2021 | [PUB] |
| Ensemble Attention Distillation for Privacy-Preserving Federated Learning | University at Buffalo | ICCV | 2021 | [PUB] [PDF] |
| Collaborative Unsupervised Visual Representation Learning from Decentralized Data | NTU; SenseTime | ICCV | 2021 | [PUB] [PDF] |
| Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification | NTU | MM | 2021 | [PUB] [PDF] |
| Federated Visual Classification with Real-World Data Distribution | MIT; Google | ECCV | 2020 | [PUB] [PDF] [VIDEO] |
| InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages | MM | 2020 | [PUB] | |
Performance Optimization of Federated Person Re-identification via Benchmark Analysis data. | NTU | MM | 2020 | [PUB] [PDF] [CODE] [解读] |
Federated Learning papers accepted by top AI and NLP conference and journal, including ACL(Annual Meeting of the Association for Computational Linguistics), NAACL(North American Chapter of the Association for Computational Linguistics), EMNLP(Conference on Empirical Methods in Natural Language Processing) and COLING(International Conference on Computational Linguistics).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems | EMNLP | 2024 | [PUB] | |
| Safely Learning with Private Data: A Federated Learning Framework for Large Language Model | EMNLP | 2024 | [PUB] | |
| FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models | EMNLP | 2024 | [PUB] | |
| Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models | EMNLP | 2024 | [PUB] | |
| Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models | EMNLP | 2024 | [PUB] | |
| Promoting Data and Model Privacy in Federated Learning through Quantized LoRA | EMNLP Findings | 2024 | [PUB] | |
| Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models | EMNLP Findings | 2024 | [PUB] | |
| Generalizable Multilingual Hate Speech Detection on Low Resource Indian Languages using Fair Selection in Federated Learning | NAACL | 2024 | [PUB] | |
| Open-Vocabulary Federated Learning with Multimodal Prototyping | NAACL | 2024 | [PUB] | |
| Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning | NAACL | 2024 | [PUB] | |
| FedLFC: Towards Efficient Federated Multilingual Modeling with LoRA-based Language Family Clustering. | NAACL Findings | 2024 | [PUB] | |
| Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning. | NAACL Findings | 2024 | [PUB] | |
| Can Public Large Language Models Help Private Cross-device Federated Learning? | NAACL Findings | 2024 | [PUB] | |
| Fair Federated Learning with Biased Vision-Language Models | ACL Findings | 2024 | [PUB] | |
| Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization | Universitas Auburn | EMNLP | 2023 | [PUB] [PDF] [CODE] |
| Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint Identification | IIT Patna | EMNLP | 2023 | [PUB] [CODE] |
| FedID: Federated Interactive Distillation for Large-Scale Pretraining Language Models | YNU | EMNLP | 2023 | [PUB] [CODE] |
| FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning | KAIST | EMNLP | 2023 | [PUB] [PDF] |
| Coordinated Replay Sample Selection for Continual Federated Learning | CMU | EMNLP industry Track | 2023 | [PUB] [PDF] |
| Tunable Soft Prompts are Messengers in Federated Learning | SYSU | EMNLP Findings | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms | OSU | ACL | 2023 | [PUB] [PDF] [CODE] |
| FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP | MEMUKUL; Peng Cheng Lab | ACL | 2023 | [PUB] [CODE] |
| Client-Customized Adaptation for Parameter-Efficient Federated Learning | ACL Findings | 2023 | [PUB] | |
| Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter | ACL Findings | 2023 | [PUB] [PDF] [CODE] | |
| Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets | ACL Findings | 2023 | [PUB] | |
| FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models | ACL Findings | 2023 | [PUB] | |
| Federated Learning of Gboard Language Models with Differential Privacy | ACL Industry Track | 2023 | [PUB] [PDF] | |
| Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling | SNU | EMNLP | 2022 | [PUB] [PDF] |
| A Federated Approach to Predicting Emojis in Hindi Tweets | University of Alberta | EMNLP | 2022 | [PUB] [PDF] [CODE] |
| Federated Model Decomposition with Private Vocabulary for Text Classification | MEMUKUL; Peng Cheng Lab | EMNLP | 2022 | [PUB] [CODE] |
| Fair NLP Models with Differentially Private Text Encoders | Univ. Lille | EMNLP | 2022 | [PUB] [PDF] [CODE] |
| Federated Continual Learning for Text Classification via Selective Inter-client Transfer | DRIMCo GmbH; LMU | EMNLP Findings | 2022 | [PUB] [PDF] [CODE] |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation kg. | Lehigh University | EMNLP Findings | 2022 | [PUB] [PDF] [CODE] |
| Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation | PKU | EMNLP Findings | 2022 | [PUB] [PDF] |
| Scaling Language Model Size in Cross-Device Federated Learning | ACL workshop | 2022 | [PUB] [PDF] | |
| Intrinsic Gradient Compression for Scalable and Efficient Federated Learning | Oxford | ACL workshop | 2022 | [PUB] [PDF] |
| ActPerFL: Active Personalized Federated Learning | Amazon | ACL workshop | 2022 | [PUB] [PAGE] |
| FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks | USC | NAACL | 2022 | [PUB] [PDF] [CODE] |
| Federated Learning with Noisy User Feedback | USC; Amazon | NAACL | 2022 | [PUB] [PDF] |
| Training Mixed-Domain Translation Models via Federated Learning | Amazon | NAACL | 2022 | [PUB] [PAGE] [PDF] |
| Pretrained Models for Multilingual Federated Learning | Johns Hopkins University | NAACL | 2022 | [PUB] [PDF] [CODE] |
| Federated Chinese Word Segmentation with Global Character Associations | Universitas Washington | ACL workshop | 2021 | [PUB] [CODE] |
| Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation | USTC | EMNLP | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
| Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories | CUHK (Shenzhen) | EMNLP | 2021 | [PUB] [CODE] [VIDEO] |
| A Secure and Efficient Federated Learning Framework for NLP | Universitas Connecticut | EMNLP | 2021 | [PUB] [PDF] [VIDEO] |
| Distantly Supervised Relation Extraction in Federated Settings | UCAS | EMNLP workshop | 2021 | [PUB] [PDF] [CODE] |
| Federated Learning with Noisy User Feedback | USC; Amazon | NAACL workshop | 2021 | [PUB] [PDF] |
| An Investigation towards Differentially Private Sequence Tagging in a Federated Framework | Universität Hamburg | NAACL workshop | 2021 | [PUB] |
| Understanding Unintended Memorization in Language Models Under Federated Learning | NAACL workshop | 2021 | [PUB] [PDF] | |
| FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction | KAS | EMNLP | 2020 | [PUB] [VIDEO] [解读] |
| Empirical Studies of Institutional Federated Learning For Natural Language Processing | Ping An Technology | EMNLP workshop | 2020 | [PUB] |
| Federated Learning for Spoken Language Understanding | PKU | COLING | 2020 | [PUB] |
| Two-stage Federated Phenotyping and Patient Representation Learning | Boston Children's Hospital Harvard Medical School | ACL workshop | 2019 | [PUB] [PDF] [CODE] [UC.] |
Federated Learning papers accepted by top Information Retrieval conference and journal, including SIGIR(Annual International ACM SIGIR Conference on Research and Development in Information Retrieval).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit | KAMIS | SIGIR | 2024 | [PUB] |
| Revisit Targeted Model Poisoning on Federated Recommendation: Optimize via Multi-objective Transport | ZJU | SIGIR | 2024 | [PUB] |
| FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation | UQ | SIGIR | 2024 | [PUB] [PDF] [CODE] |
| FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction | Grup Alibaba | SIGIR | 2024 | [PUB] |
| Personalized Federated Relation Classification over Heterogeneous Texts | NUDT | SIGIR | 2023 | [PUB] |
| Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity | SDU | SIGIR | 2023 | [PUB] |
| Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures | UQ | SIGIR | 2023 | [PUB] [PDF] |
| FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning | Grup Alibaba | SIGIR | 2023 | [PUB] [PDF] [CODE] |
| Edge-cloud Collaborative Learning with Federated and Centralized Features (short-paper) | ZJU | SIGIR | 2023 | [PUB] [PDF] |
| FLIRT: Federated Learning for Information Retrieval (extended-abstract) | IMT Lucca | SIGIR | 2023 | [PUB] |
| Is Non-IID Data a Threat in Federated Online Learning to Rank? | The University of Queensland | SIGIR | 2022 | [PUB] [CODE] |
| FedCT: Federated Collaborative Transfer for Recommendation | Rutgers University | SIGIR | 2021 | [PUB] [PDF] [CODE] |
| On the Privacy of Federated Pipelines | Universitas Teknik Munich | SIGIR | 2021 | [PUB] |
| FedCMR: Federated Cross-Modal Retrieval. | Dalian University of Technology | SIGIR | 2021 | [PUB] [CODE] |
| Meta Matrix Factorization for Federated Rating Predictions. | SDU | SIGIR | 2020 | [PUB] [PDF] |
Federated Learning papers accepted by top Database conference and journal, including SIGMOD(ACM SIGMOD Conference) , ICDE(IEEE International Conference on Data Engineering) and VLDB(Very Large Data Bases Conference).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| FedMix: Boosting with Data Mixture for Vertical Federated Learning | ICDE | 2024 | [PUB] | |
| FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation | ICDE | 2024 | [PUB] | |
| Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning | ICDE | 2024 | [PUB] | |
| Semi-Asynchronous Online Federated Crowdsourcing | ICDE | 2024 | [PUB] | |
| AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity | ICDE | 2024 | [PUB] | |
| MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation | ICDE | 2024 | [PUB] | |
| LightTR: A Lightweight Framework for Federated Trajectory Recovery | ICDE | 2024 | [PUB] | |
| Feed: Towards Personalization-Effective Federated Learning | ICDE | 2024 | [PUB] | |
| Label Noise Correction for Federated Learning: A Secure, Efficient and Reliable Realization | ICDE | 2024 | [PUB] | |
| Fast, Robust and Interpretable Participant Contribution Estimation for Federated Learning | ICDE | 2024 | [PUB] | |
| HeteFedRec: Federated Recommender Systems with Model Heterogeneity | ICDE | 2024 | [PUB] | |
| Hide Your Model: A Parameter Transmission-free Federated Recommender System | ICDE | 2024 | [PUB] | |
| FedCTQ: A Federated-Based Framework for Accurate and Efficient Contact Tracing Query | ICDE | 2024 | [PUB] | |
| Preventing the Popular Item Embedding Based Attack in Federated Recommendations | ICDE | 2024 | [PUB] | |
| RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection | ICDE | 2024 | [PUB] | |
| Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly | TUM | DEEM@SIGMOD | 2024 | [PUB] |
| FedSQ: A Secure System for Federated Vector Similarity Queries | VLDB | 2024 | [PUB] | |
| FedSM: A Practical Federated Shared Mobility System | VLDB | 2024 | [PUB] | |
| OFL-W3: A One-Shot Federated Learning System on Web 3.0 | VLDB | 2024 | [PUB] | |
| Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation | VLDB | 2024 | [PUB] | |
| OFL-W3: A One-shot Federated Learning System on Web 3.0 | VLDB | 2024 | [PUB] | |
| Uldp-FL: Federated Learning with Across Silo User-Level Differential Privacy. | VLDB | 2024 | [PUB] | |
| FedSM: A Practical Federated Shared Mobility System. | VLDB | 2024 | [PUB] | |
| FedSQ: A Secure System for Federated Vector Similarity Queries | VLDB | 2024 | [PUB] | |
| Performance-Based Pricing of Federated Learning via Auction | Grup Alibaba | VLDB | 2024 | [PUB] [CODE] |
| A Blockchain System for Clustered Federated Learning with Peer-to-Peer Knowledge Transfer | NJU | VLDB | 2024 | [PUB] [CODE] |
| Communication Efficient and Provable Federated Unlearning | SDU; KAUST | VLDB | 2024 | [PUB] [PDF] [CODE] |
| Enhancing Decentralized Federated Learning for Non-IID Data on Heterogeneous Devices | USTC | ICDE | 2023 | [PUB] |
| Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs | Columbia University | ICDE | 2023 | [PUB] [CODE] |
| FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge | SEDIKIT | ICDE | 2023 | [PUB] [PDF] |
| Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices | SJTU | ICDE | 2023 | [PUB] [PDF] |
| Federated IoT Interaction Vulnerability Analysis | MSU | ICDE | 2023 | [PUB] |
| Distribution-Regularized Federated Learning on Non-IID Data | BUAA | ICDE | 2023 | [PUB] |
| Fed-SC: One-Shot Federated Subspace Clustering over High-Dimensional Data | ShanghaiTech University | ICDE | 2023 | [PUB] [CODE] |
| FLBooster: A Unified and Efficient Platform for Federated Learning Acceleration | ZJU | ICDE | 2023 | [PUB] |
| FedGTA: Topology-aware Averaging for Federated Graph Learning. | SEDIKIT | VLDB | 2023 | [PUB] [CODE] |
| FS-Real: A Real-World Cross-Device Federated Learning Platform. | Grup Alibaba | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Federated Calibration and Evaluation of Binary Classifiers. | meta | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification. | Universitas Kyoto | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System. | NUS | VLDB | 2023 | [PUB] [CODE] |
| Differentially Private Vertical Federated Clustering. | Purdue University | VLDB | 2023 | [PUB] [PDF] [CODE] |
| FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. | Alibaba | VLDB | 2023 | [PUB] [PDF] [CODE] |
| Secure Shapley Value for Cross-Silo Federated Learning. | Universitas Kyoto | VLDB | 2023 | [PUB] [PDF] [CODE] |
| OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving Desensitization | ZJU | VLDB | 2022 | [PUB] [PDF] [CODE] |
| Skellam Mixture Mechanism: a Novel Approach to Federated Learning with Differential Privacy. | NUS | VLDB | 2022 | [PUB] [CODE] |
| Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update | PKU | VLDB | 2022 | [PUB] [PDF] [CODE] |
| FedTSC: A Secure Federated Learning System for Interpretable Time Series Classification. | MEMUKUL | VLDB | 2022 | [PUB] [CODE] |
| Improving Fairness for Data Valuation in Horizontal Federated Learning | The UBC | ICDE | 2022 | [PUB] [PDF] |
| FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity | USTC | ICDE | 2022 | [PUB] [PDF] [CODE] |
| FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing. | USTC | ICDE | 2022 | [PUB] |
| Federated Learning on Non-IID Data Silos: An Experimental Study. | NUS | ICDE | 2022 | [PUB] [PDF] [CODE] |
| Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing | USTC | ICDE | 2022 | [PUB] |
| Samba: A System for Secure Federated Multi-Armed Bandits | Univ. Clermont Auvergne | ICDE | 2022 | [PUB] [CODE] |
| FedRecAttack: Model Poisoning Attack to Federated Recommendation | ZJU | ICDE | 2022 | [PUB] [PDF] [CODE] |
| Enhancing Federated Learning with In-Cloud Unlabeled Data | USTC | ICDE | 2022 | [PUB] |
| Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning | USTC | ICDE | 2022 | [PUB] |
| An Introduction to Federated Computation | University of Warwick; Facebook | SIGMOD Tutorial | 2022 | [PUB] |
| BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | PKU; Tencent | SIGMOD | 2022 | [PUB] [PDF] |
| An Efficient Approach for Cross-Silo Federated Learning to Rank | BUAA | ICDE | 2021 | [PUB] [RELATED PAPER(ZH)] |
| Feature Inference Attack on Model Predictions in Vertical Federated Learning | NUS | ICDE | 2021 | [PUB] [PDF] [CODE] |
| Efficient Federated-Learning Model Debugging | USTC | ICDE | 2021 | [PUB] |
| Federated Matrix Factorization with Privacy Guarantee | Purdue | VLDB | 2021 | [PUB] |
| Projected Federated Averaging with Heterogeneous Differential Privacy. | Renmin University of China | VLDB | 2021 | [PUB] [CODE] |
| Enabling SQL-based Training Data Debugging for Federated Learning | Simon Fraser University | VLDB | 2021 | [PUB] [PDF] [CODE] |
| Refiner: A Reliable Incentive-Driven Federated Learning System Powered by Blockchain | ZJU | VLDB | 2021 | [PUB] |
| Tanium Reveal: A Federated Search Engine for Querying Unstructured File Data on Large Enterprise Networks | Tanium Inc. | VLDB | 2021 | [PUB] [VIDEO] |
| VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning | PKU | SIGMOD | 2021 | [PUB] |
| ExDRa: Exploratory Data Science on Federated Raw Data | SIEMENS | SIGMOD | 2021 | [PUB] |
| Joint blockchain and federated learning-based offloading in harsh edge computing environments | TJU | SIGMOD workshop | 2021 | [PUB] |
| Privacy Preserving Vertical Federated Learning for Tree-based Models | NUS | VLDB | 2020 | [PUB] [PDF] [VIDEO] [CODE] |
Federated Learning papers accepted by top Database conference and journal, including SIGCOMM(Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication), INFOCOM(IEEE Conference on Computer Communications), MobiCom(ACM/IEEE International Conference on Mobile Computing and Networking), NSDI(Symposium on Networked Systems Design and Implementation) and WWW(The Web Conference).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning | INFOCOM | 2024 | [PUB] | |
| Strategic Data Revocation in Federated Unlearning | INFOCOM | 2024 | [PUB] | |
| FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding | INFOCOM | 2024 | [PUB] | |
| Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization | INFOCOM | 2024 | [PUB] | |
| FairFed: Improving Fairness and Efficiency of Contribution Evaluation in Federated Learning via Cooperative Shapley Value | INFOCOM | 2024 | [PUB] | |
| DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service | INFOCOM | 2024 | [PUB] | |
| Tomtit: Hierarchical Federated Fine-Tuning of Giant Models based on Autonomous Synchronization | INFOCOM | 2024 | [PUB] | |
| BR-DeFedRL: Byzantine-Robust Decentralized Federated Reinforcement Learning with Fast Convergence and Communication Efficiency | INFOCOM | 2024 | [PUB] | |
| Titanic: Towards Production Federated Learning with Large Language Models | INFOCOM | 2024 | [PUB] | |
| Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression | INFOCOM | 2024 | [PUB] | |
| Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes | INFOCOM | 2024 | [PUB] | |
| Federated Analytics-Empowered Frequent Pattern Mining for Decentralized Web 3.0 Applications | INFOCOM | 2024 | [PUB] | |
| GraphProxy: Communication-Efficient Federated Graph Learning with Adaptive Proxy | INFOCOM | 2024 | [PUB] | |
| Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration | INFOCOM | 2024 | [PUB] [CODE] | |
| AeroRec: An Efficient On-Device Recommendation Framework using Federated Self-Supervised Knowledge Distillation | INFOCOM | 2024 | [PUB] | |
| Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning | INFOCOM | 2024 | [PUB] | |
| Heroes: Lightweight Federated Learning with Neural Composition and Adaptive Local Update in Heterogeneous Edge Networks | INFOCOM | 2024 | [PUB] | |
| Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiency | INFOCOM | 2024 | [PUB] | |
| Federated Offline Policy Optimization with Dual Regularization | INFOCOM | 2024 | [PUB] | |
| A Semi-Asynchronous Decentralized Federated Learning Framework via Tree-Graph Blockchain | INFOCOM | 2024 | [PUB] | |
| SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation | INFOCOM | 2024 | [PUB] | |
| Towards Efficient Asynchronous Federated Learning in Heterogeneous Edge Environments | INFOCOM | 2024 | [PUB] | |
| Federated Learning Based Integrated Sensing, Communications, and Powering Over 6G Massive-MIMO Mobile Networks | INFOCOM workshop | 2024 | [PUB] | |
| Decentralized Federated Learning Under Free-riders: Credibility Analysis | INFOCOM workshop | 2024 | [PUB] | |
| TrustBandit: Optimizing Client Selection for Robust Federated Learning Against Poisoning Attacks | INFOCOM workshop | 2024 | [PUB] | |
| Cascade: Enhancing Reinforcement Learning with Curriculum Federated Learning and Interference Avoidance — A Case Study in Adaptive Bitrate Selection | INFOCOM workshop | 2024 | [PUB] | |
| Efficient Adapting for Vision-language Foundation Model in Edge Computing Based on Personalized and Multi-Granularity Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| Distributed Link Heterogeneity Exploitation for Attention-Weighted Robust Federated Learning in 6G Networks | INFOCOM workshop | 2024 | [PUB] | |
| GAN-Based Privacy Abuse Attack on Federated Learning in IoT Networks | INFOCOM workshop | 2024 | [PUB] | |
| Fedkit: Enabling Cross-Platform Federated Learning for Android and iOS | INFOCOM workshop | 2024 | [PUB] [CODE] | |
| ASR-FED: Agnostic Straggler Resilient Federated Algorithm for Drone Networks Security | INFOCOM workshop | 2024 | [PUB] | |
| Unbiased Federated Learning for Heterogeneous Data Under Unreliable Links | INFOCOM workshop | 2024 | [PUB] | |
| Efficient Client Sampling with Compression in Heterogeneous Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| Reputation-Aware Scheduling for Secure Internet of Drones: A Federated Multi-Agent Deep Reinforcement Learning Approach | INFOCOM workshop | 2024 | [PUB] | |
| Two-Timescale Energy Optimization for Wireless Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| A Data Reconstruction Attack Against Vertical Federated Learning Based on Knowledge Transfer | INFOCOM workshop | 2024 | [PUB] | |
| Federated Learning for Energy-efficient Cooperative Perception in Connected and Autonomous Vehicles | INFOCOM workshop | 2024 | [PUB] | |
| Federated Learning-Based Cooperative Model Training for Task-Oriented Semantic Communication | INFOCOM workshop | 2024 | [PUB] | |
| FedBF16-Dynamic: Communication-Efficient Federated Learning with Adaptive Transmission | INFOCOM workshop | 2024 | [PUB] | |
| Designing Robust 6G Networks with Bimodal Distribution for Decentralized Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| Federated Distributed Deep Reinforcement Learning for Recommendation-enabled Edge Caching | INFOCOM workshop | 2024 | [PUB] | |
| Joint Optimization of Charging Time and Resource Allocation in Wireless Power Transfer Assisted Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| Joint Client Selection and Privacy Compensation for Differentially Private Federated Learning | INFOCOM workshop | 2024 | [PUB] | |
| Wireless Hierarchical Federated Aggregation Weights Design with Loss-Based-Heterogeneity | INFOCOM workshop | 2024 | [PUB] | |
| ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease | CUHK | MobiCom | 2024 | [PUB] [PDF] [CODE] |
| Accelerating the Decentralized Federated Learning via Manipulating Edges | SZU | WWW | 2024 | [PUB] |
| Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation | SDNU | WWW | 2024 | [PUB] [PDF] [CODE] |
| PAGE: Equilibrate Personalization and Generalization in Federated Learning | XDU | WWW | 2024 | [PUB] [PDF] [CODE] |
| Federated Learning Vulnerabilities: Privacy Attacks with Denoising Diffusion Probabilistic Models | ECNU | WWW | 2024 | [PUB] |
| Co-clustering for Federated Recommender System | UIUC | WWW | 2024 | [PUB] |
| Incentive and Dynamic Client Selection for Federated Unlearning | BUPT | WWW | 2024 | [PUB] |
| Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training | VinUniversity | WWW | 2024 | [PUB] [PDF] [CODE] |
| BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework | ZJU | WWW | 2024 | [PUB] [PDF] |
| Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation | UQ | WWW | 2024 | [PUB] [PDF] |
| FedDSE: Distribution-aware Sub-model Extraction for Federated Learning over Resource-constrained Devices | NTU | WWW | 2024 | [PUB] |
| Cardinality Counting in "Alcatraz": A Privacy-aware Federated Learning Approach | CSIRO's Data61 | WWW | 2024 | [PUB] |
| Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation | BUPT | WWW | 2024 | [PUB] [PDF] |
| Poisoning Federated Recommender Systems with Fake Users | USTC | WWW | 2024 | [PUB] [PDF] |
| Towards Energy-efficient Federated Learning via INT8-based Training on Mobile DSPs | BUPT | WWW | 2024 | [PUB] |
| Privacy-Preserving and Fairness-Aware Federated Learning for Critical Infrastructure Protection and Resilience | UTS | WWW | 2024 | [PUB] [CODE] |
| When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions | JLU | WWW | 2024 | [PUB] [PDF] [CODE] |
| How Few Davids Improve One Goliath: Federated Learning in Resource-Skewed Edge Computing Environments | UCSD | WWW | 2024 | [PUB] [CODE] [VIDEO] |
| Poisoning Attack on Federated Knowledge Graph Embedding | PolyU | WWW | 2024 | [PUB] [CODE] |
| FL@FM-TheWebConf'24: International Workshop on Federated Foundation Models for the Web | CUHK | WWW (Companion Volume) | 2024 | [PUB] [PAGE] |
| An Investigation into the Feasibility of Performing Federated Learning on Social Linked Data Servers | University of Southampton | WWW (Companion Volume) | 2024 | [PUB] |
| Exploring Representational Similarity Analysis to Protect Federated Learning from Data Poisoning | SYSU | WWW (Companion Volume) | 2024 | [PUB] |
| Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation | Purdue University | WWW (Companion Volume) | 2024 | [PUB] [PDF] |
| FedHLT: Efficient Federated Low-Rank Adaption with Hierarchical Language Tree for Multilingual Modeling | CUHK | WWW (Companion Volume) | 2024 | [PUB] |
| HBIAS FedAvg: Smooth Federated Learning Transition for In-use Edge Models | IIT | WWW (Companion Volume) | 2024 | [PUB] |
| Phoenix: A Federated Generative Diffusion Model | UW | WWW (Companion Volume) | 2024 | [PUB] |
| Federated Learning in Large Model Era: Vision-Language Model for Smart City Safety Operation Management | ENN; UPC | WWW (Companion Volume) | 2024 | [PUB] |
| Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks | USTC | WWW (Companion Volume) | 2024 | [PUB] [PDF] |
| GradFilt: Class-wise Targeted Data Reconstruction from Gradients in Federated Learning | PolyU | WWW (Companion Volume) | 2024 | [PUB] |
| Detecting Poisoning Attacks on Federated Learning Using Gradient-Weighted Class Activation Mapping | ISEP | WWW (Companion Volume) | 2024 | [PUB] |
| AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving | NTU | MobiCom | 2023 | [PUB] [PDF] |
| Efficient Federated Learning for Modern NLP | Beiyou Shenzhen Institute | MobiCom | 2023 | [PDF] [解读] |
| FLASH: Towards a High-performance Hardware Acceleration Architecture for Cross-silo Federated Learning | HKUST; Clustar | NSDI | 2023 | [PUB] [SLIDE] [VIDEO] |
| To Store or Not? Online Data Selection for Federated Learning with Limited Storage. | SJTU | WWW | 2023 | [PUB] [PDF] |
| pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning. | PolyU | WWW | 2023 | [PUB] |
| Quantifying and Defending against Privacy Threats on Federated Knowledge Graph Embedding. | ZJU; HIC-ZJU | WWW | 2023 | [PUB] [PDF] |
| Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks | PKU | WWW | 2023 | [PUB] [PDF] [CODE] |
| Semi-decentralized Federated Ego Graph Learning for Recommendation | SUST | WWW | 2023 | [PUB] [PDF] |
| FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous Model Architectures. | Swinburne | WWW | 2023 | [PUB] [CODE] |
| FedEdge: Accelerating Edge-Assisted Federated Learning. | Swinburne | WWW | 2023 | [PUB] |
| Federated Node Classification over Graphs with Latent Link-type Heterogeneity. | Emory University | WWW | 2023 | [PUB] [CODE] |
| FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection. | USTC | WWW | 2023 | [PUB] [PDF] [CODE] |
| Interaction-level Membership Inference Attack Against Federated Recommender Systems. | UQ | WWW | 2023 | [PUB] [PDF] |
| AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated Learning. | Deakin University | WWW | 2023 | [PUB] |
| Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning. | NJU | WWW | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning for Metaverse: A Survey. | JNU | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
| Understanding the Impact of Label Skewness and Optimization on Federated Learning for Text Classification | KU Leuven | WWW (Companion Volume) | 2023 | [PUB] |
| Privacy-Preserving Online Content Moderation: A Federated Learning Use Case. | MEMOTONG | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
| Privacy-Preserving Online Content Moderation with Federated Learning. | MEMOTONG | WWW (Companion Volume) | 2023 | [PUB] |
| A Federated Learning Benchmark for Drug-Target Interaction. | University of Turin | WWW (Companion Volume) | 2023 | [PUB] [PDF] [CODE] |
| Towards a Decentralized Data Hub and Query System for Federated Dynamic Data Spaces. | TU Berlin | WWW (Companion Volume) | 2023 | [PUB] |
| 1st Workshop on Federated Learning Technologies1st Workshop on Federated Learning Technologies | University of Turin | WWW (Companion Volume) | 2023 | [PUB] |
| A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy | CUHK | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
| A Hierarchical Knowledge Transfer Framework for Heterogeneous Federated Learning | KAMIS | INFOCOM | 2023 | [PUB] |
| A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning | Southeast University | INFOCOM | 2023 | [PUB] |
| Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning | USTC | INFOCOM | 2023 | [PUB] [PDF] |
| AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices | Guangdong University of Technology | INFOCOM | 2023 | [PUB] [PDF] |
| AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation | HUST | INFOCOM | 2023 | [PUB] |
| Asynchronous Federated Unlearning | University of Toronto | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
| Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization | PSU | INFOCOM | 2023 | [PUB] [PDF] |
| Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing | Beihang University | INFOCOM | 2023 | [PUB] |
| Federated Learning under Heterogeneous and Correlated Client Availability | Inria | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning with Flexible Control | IBM | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
| Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks | The University of Sydney | INFOCOM | 2023 | [PUB] [PDF] |
| FedMoS: Taming Client Drift in Federated Learning with Double Momentum and Adaptive Selection | HUST | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
| FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning | NTU | INFOCOM | 2023 | [PUB] [PDF] |
| Heterogeneity-Aware Federated Learning with Adaptive Client Selection and Gradient Compression | USTC | INFOCOM | 2023 | |
| Joint Edge Aggregation and Association for Cost-Efficient Multi-Cell Federated Learning | NUDT | INFOCOM | 2023 | [PUB] |
| Joint Participation Incentive and Network Pricing Design for Federated Learning | Universitas Barat Laut | INFOCOM | 2023 | [PUB] |
| More than Enough is Too Much: Adaptive Defenses against Gradient Leakage in Production Federated Learning | University of Toronto | INFOCOM | 2023 | [PUB] [PDF] [WEIBO] |
| Network Adaptive Federated Learning: Congestion and Lossy Compression | UTAustin | INFOCOM | 2023 | [PUB] [PDF] |
| OBLIVION: Poisoning Federated Learning by Inducing Catastrophic Forgetting | The Hang Seng University of Hong Kong | INFOCOM | 2023 | [PUB] [CODE] |
| Privacy as a Resource in Differentially Private Federated Learning | BUPT | INFOCOM | 2023 | [PUB] |
| SplitGP: Achieving Both Generalization and Personalization in Federated Learning | KAIST | INFOCOM | 2023 | [PUB] [PDF] |
| SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition | Beihang University | INFOCOM | 2023 | [PUB] |
| Tackling System Induced Bias in Federated Learning: Stratification and Convergence Analysis | Southern University of Science and Technology | INFOCOM | 2023 | [PUB] [PDF] |
| Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions | BUPT | INFOCOM | 2023 | [PUB] [PDF] |
| Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data Labeling | Universitas Auburn | INFOCOM | 2023 | [PUB] [PDF] |
| TVFL: Tunable Vertical Federated Learning towards Communication-Efficient Model Serving | USTC | INFOCOM | 2023 | [PUB] |
| PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning | MSU | MobiCom | 2022 | [PUB] [PDF] [CODE] |
| NestFL: efficient federated learning through progressive model pruning in heterogeneous edge computing | pmlabs | MobiCom(Poster) | 2022 | [PUB] |
| Federated learning-based air quality prediction for smart cities using BGRU model | IITM | MobiCom(Poster) | 2022 | [PUB] |
| FedHD: federated learning with hyperdimensional computing | UCSD | MobiCom(Demo) | 2022 | [PUB] [CODE] |
| Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks | Korea University | INFOCOM | 2022 | [PUB] |
| Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending | University of Toronto | INFOCOM | 2022 | [PUB] |
| Optimal Rate Adaption in Federated Learning with Compressed Communications | SZU | INFOCOM | 2022 | [PUB] [PDF] |
| The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining. | CityU | INFOCOM | 2022 | [PUB] [PDF] |
| Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling. | CUHK; AIRS ;Yale University | INFOCOM | 2022 | [PUB] [PDF] |
| Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization | Army Research Laboratory, Adelphi | INFOCOM | 2022 | [PUB] [PDF] |
| FLASH: Federated Learning for Automated Selection of High-band mmWave Sectors | BARU | INFOCOM | 2022 | [PUB] [CODE] |
| A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning | CUHK; AIRS | INFOCOM | 2022 | [PUB] |
| Protect Privacy from Gradient Leakage Attack in Federated Learning | PolyU | INFOCOM | 2022 | [PUB] [SLIDE] |
| FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent Pattern Mining. | SJTU | INFOCOM | 2022 | [PUB] [CODE] |
| An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning | SWJTU;THU | WWW | 2022 | [PUB] [PDF] [CODE] |
| LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning | Universitas Yonsei | WWW | 2022 | [PUB] |
| Federated Unlearning via Class-Discriminative Pruning | PolyU | WWW | 2022 | [PUB] [PDF] [CODE] |
| FedKC: Federated Knowledge Composition for Multilingual Natural Language Understanding | Purdue | WWW | 2022 | [PUB] |
| Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing. | WWW (Companion Volume) | 2022 | ||
| Federated Bandit: A Gossiping Approach | Universitas Kalifornia | SIGMETRICS | 2021 | [PUB] [PDF] |
| Hermes: an efficient federated learning framework for heterogeneous mobile clients | Duke University | MobiCom | 2021 | [PUB] |
| Federated mobile sensing for activity recognition | Samsung AI Center | MobiCom | 2021 | [PUB] [PAGE] [TALKS] [VIDEO] |
| Learning for Learning: Predictive Online Control of Federated Learning with Edge Provisioning. | Nanjing University | INFOCOM | 2021 | [PUB] |
| Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation. | Purdue | INFOCOM | 2021 | [PUB] [PDF] |
| FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation | KAMIS | INFOCOM | 2021 | [PUB] |
| Sample-level Data Selection for Federated Learning | USTC | INFOCOM | 2021 | [PUB] |
| To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices | Xidian University; KAS | INFOCOM | 2021 | [PUB] [PDF] |
| Cost-Effective Federated Learning Design | CUHK; AIRS; Universitas Yale | INFOCOM | 2021 | [PUB] [PDF] |
| An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective | The UBC | INFOCOM | 2021 | [PUB] |
| Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing | USTC | INFOCOM | 2021 | [PUB] |
| FedServing: A Federated Prediction Serving Framework Based on Incentive Mechanism. | Jinan University; CityU | INFOCOM | 2021 | [PUB] [PDF] |
| Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach | Arizona State University | INFOCOM | 2021 | [PUB] [PDF] |
| Dual Attention-Based Federated Learning for Wireless Traffic Prediction | King Abdullah University of Science and Technology | INFOCOM | 2021 | [PUB] [PDF] [CODE] |
| FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing | University of Notre Dame | INFOCOM | 2021 | [PUB] |
| P-FedAvg: Parallelizing Federated Learning with Theoretical Guarantees | SYSU; Guangdong Key Laboratory of Big Data Analysis and Processing | INFOCOM | 2021 | [PUB] |
| Meta-HAR: Federated Representation Learning for Human Activity Recognition. | University of Alberta | WWW | 2021 | [PUB] [PDF] [CODE] |
| PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization | PKU | WWW | 2021 | [PUB] [PDF] [CODE] |
| Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics | emosi | WWW | 2021 | [PUB] [CODE] |
| Hierarchical Personalized Federated Learning for User Modeling | USTC | WWW | 2021 | [PUB] |
| Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data | PKU | WWW | 2021 | [PUB] [PDF] [SLIDE] [CODE] |
| Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction | SYSU | WWW | 2021 | [PUB] |
| Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled Wireless Networks. | Nanjing University | INFOCOM | 2020 | [PUB] |
| Optimizing Federated Learning on Non-IID Data with Reinforcement Learning | University of Toronto | INFOCOM | 2020 | [PUB] [SLIDE] [CODE] [解读] |
| Enabling Execution Assurance of Federated Learning at Untrusted Participants | KAMIS | INFOCOM | 2020 | [PUB] [CODE] |
| Billion-scale federated learning on mobile clients: a submodel design with tunable privacy | SJTU | MobiCom | 2020 | [PUB] |
| Federated Learning over Wireless Networks: Optimization Model Design and Analysis | The University of Sydney | INFOCOM | 2019 | [PUB] [CODE] |
| Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning | Wuhan University | INFOCOM | 2019 | [PUB] [PDF] [UC.] |
| InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential Privacy | Collaborative Innovation Center of Geospatial Technology | INFOCOM | 2018 | [PUB] |
Federated Learning papers accepted by top Database conference and journal, including OSDI(USENIX Symposium on Operating Systems Design and Implementation), SOSP(Symposium on Operating Systems Principles), ISCA(International Symposium on Computer Architecture), MLSys(Conference on Machine Learning and Systems), EuroSys(European Conference on Computer Systems), TPDS(IEEE Transactions on Parallel and Distributed Systems), DAC(Design Automation Conference), TOCS(ACM Transactions on Computer Systems), TOS(ACM Transactions on Storage), TCAD(IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems), TC(IEEE Transactions on Computers).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems. | DAC | 2024 | [PUB] | |
| Fake Node-Based Perception Poisoning Attacks against Federated Object Detection Learning in Mobile Computing Networks | DAC | 2024 | [PUB] | |
| Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation | ISCA | 2024 | [PUB] | |
| FedTrans: Efficient Federated Learning via Multi-Model Transformation | UIUC | MLSys | 2024 | [PUB] [PDF] |
| LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning | UC Riverside | MLSys | 2024 | [PUB] [PDF] |
| HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning | Korea University | MLSys | 2024 | [PUB] [PDF] [CODE] |
| DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation | IBM Research | EuroSys | 2024 | [PUB] |
| FLOAT: Federated Learning Optimizations with Automated Tuning | Virginia Tech | EuroSys | 2024 | [PUB] [CODE] |
| Totoro: A Scalable Federated Learning Engine for the Edge | UCSC | EuroSys | 2024 | [PUB] |
| Dordis: Efficient Federated Learning with Dropout-Resilient Differential Privacy | HKUST | EuroSys | 2024 | [PUB] [PDF] [CODE] |
| FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN | EuroSys workshop | 2024 | [PUB] | |
| ALS Algorithm for Robust and Communication-Efficient Federated Learning | EuroSys workshop | 2024 | [PUB] | |
| FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission. | EuroSys workshop | 2024 | [PUB] | |
| Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting. | TPDS | 2024 | [PUB] | |
| SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning | TPDS | 2024 | [PUB] | |
| FedVeca: Federated Vectorized Averaging on Non-IID Data With Adaptive Bi-Directional Global Objective | TPDS | 2024 | [PUB] | |
| Trusted Model Aggregation With Zero-Knowledge Proofs in Federated Learning. | TPDS | 2024 | [PUB] | |
| Accelerating Communication-Efficient Federated Multi-Task Learning With Personalization and Fairness. | TPDS | 2024 | [PUB] | |
| Privacy-Preserving Data Selection for Horizontal and Vertical Federated Learning. | TPDS | 2024 | [PUB] | |
| High-Performance Hardware Acceleration Architecture for Cross-Silo Federated Learning | TPDS | 2024 | [PUB] | |
| Joint Participant and Learning Topology Selection for Federated Learning in Edge Clouds | TPDS | 2024 | [PUB] | |
| Synchronize Only the Immature Parameters: Communication-Efficient Federated Learning By Freezing Parameters Adaptively | SJTU | TPDS | 2024 | [PUB] |
| FedREM: Guided Federated Learning in the Presence of Dynamic Device Unpredictability | SYSU | TPDS | 2024 | [PUB] |
| Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated Learning | IITP | TPDS | 2024 | [PUB] [PDF] |
| Taking Advantage of the Mistakes: Rethinking Clustered Federated Learning for IoT Anomaly Detection | UVIC | TPDS | 2024 | [PUB] |
| FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing | UCAS | TPDS | 2024 | [PUB] [PDF] |
| Collaboration in Federated Learning With Differential Privacy: A Stackelberg Game Analysis | SYSU | TPDS | 2024 | [PUB] |
| FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training | USTC | TPDS | 2024 | [PUB] |
| EcoFed: Efficient Communication for DNN Partitioning-Based Federated Learning | University of St Andrews | TPDS | 2024 | [PUB] [PDF] [CODE] |
| FedHAP: Federated Hashing With Global Prototypes for Cross-Silo Retrieval | KAMIS | TPDS | 2024 | [PUB] [PDF] |
| FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios. | TCAD | 2024 | [PUB] | |
| Personalized Meta-Federated Learning for IoT-Enabled Health Monitoring | TCAD | 2024 | [PUB] | |
| NebulaFL: Self-Organizing Efficient Multilayer Federated Learning Framework With Adaptive Load Tuning in Heterogeneous Edge Systems | TCAD | 2024 | [PUB] | |
| CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance | TCAD | 2024 | [PUB] | |
| FedStar: Efficient Federated Learning on Heterogeneous Communication Networks | USTC | TCAD | 2024 | [PUB] |
| Lithography Hotspot Detection Based on Heterogeneous Federated Learning With Local Adaptation and Feature Selection | ZJU | TCAD | 2024 | [PUB] [PDF] |
| FedComp: A Federated Learning Compression Framework for Resource-Constrained Edge Computing Devices | MEMUKUL | TCAD | 2024 | [PUB] |
| BSR-FL: An Efficient Byzantine-Robust Privacy-Preserving Federated Learning Framework | karena | 2024 | [PUB] | |
| User-Distribution-Aware Federated Learning for Efficient Communication and Fast Inference | ECNU; SHU | karena | 2024 | [PUB] |
| FedRFQ: Prototype-Based Federated Learning With Reduced Redundancy, Minimal Failure, and Enhanced Quality | SDU | karena | 2024 | [PUB] [PDF] |
| Value of Information: A Comprehensive Metric for Client Selection in Federated Edge Learning | SDU | karena | 2024 | [PUB] |
| Age-Aware Data Selection and Aggregator Placement for Timely Federated Continual Learning in Mobile Edge Computing | DLUT | karena | 2024 | [PUB] |
| FedGKD: Toward Heterogeneous Federated Learning via Global Knowledge Distillation | HUST | karena | 2024 | [PUB] [PDF] |
| Digital Twin-Assisted Federated Learning Service Provisioning Over Mobile Edge Networks | SDU | karena | 2024 | [PUB] |
| REFL: Resource-Efficient Federated Learning | QMUL | EuroSys | 2023 | [PUB] [PDF] [CODE] |
| A First Look at the Impact of Distillation Hyper-Parameters in Federated Knowledge Distillation | EuroSys workshop | 2023 | [PUB] | |
| Towards Practical Few-shot Federated NLP | EuroSys workshop | 2023 | [PUB] | |
| Can Fair Federated Learning Reduce the need for Personalisation? | EuroSys workshop | 2023 | [PUB] | |
| Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates | EuroSys workshop | 2023 | [PUB] | |
| Towards Robust and Bias-free Federated Learning | EuroSys workshop | 2023 | [PUB] | |
| FedTree: A Federated Learning System For Trees | UC Berkeley | MLSys | 2023 | [PUB] [CODE] |
| FLINT: A Platform for Federated Learning Integration | MLSys | 2023 | [PUB] [PDF] | |
| On Noisy Evaluation in Federated Hyperparameter Tuning | CMU | MLSys | 2023 | [PUB] [PDF] [CODE] |
| GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning | UBC | MLSys | 2023 | [PUB] [PDF] [CODE] |
| Self-Supervised On-Device Federated Learning From Unlabeled Streams. | FDU | TCAD | 2023 | [PUB] [PDF] |
| Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing | ECNU | TCAD | 2023 | [PUB] |
| Lightweight Blockchain-Empowered Secure and Efficient Federated Edge Learning | University of Exeter | karena | 2023 | [PUB] |
| Towards Data-Independent Knowledge Transfer in Model-Heterogeneous Federated Learning | PolyU | karena | 2023 | [PUB] |
| A New Federated Scheduling Algorithm for Arbitrary-Deadline DAG Tasks | NEFU | karena | 2023 | [PUB] |
| Privacy-Enhanced Decentralized Federated Learning at Dynamic Edge | SDU | karena | 2023 | [PUB] |
| Byzantine-Resilient Federated Learning at Edge | SDU | karena | 2023 | [PUB] [PDF] |
| PrivAim: A Dual-Privacy Preserving and Quality-Aware Incentive Mechanism for Federated Learning | CSU | karena | 2023 | [PUB] |
| Accelerating Federated Learning With a Global Biased Optimiser | University of Exeter | karena | 2023 | [PUB] [PDF] [CODE] |
| Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms | TU Dortmund University | karena | 2023 | [PUB] [CODE] |
| Sandbox Computing: A Data Privacy Trusted Sharing Paradigm Via Blockchain and Federated Learning. | BUPT | karena | 2023 | [PUB] |
| CHEESE: Distributed Clustering-Based Hybrid Federated Split Learning Over Edge Networks | SUDA | TPDS | 2023 | [PUB] |
| Hierarchical Federated Learning With Momentum Acceleration in Multi-Tier Networks | University of Sydney | TPDS | 2023 | [PUB] [PDF] |
| Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation | Xidian University | TPDS | 2023 | [PUB] [PDF] [CODE] |
| Collaborative Intrusion Detection System for SDVN: A Fairness Federated Deep Learning Approach | Anhui University | TPDS | 2023 | [PUB] |
| Energy-Aware, Device-to-Device Assisted Federated Learning in Edge Computing | ANU | TPDS | 2023 | [PUB] |
| Faster Federated Learning With Decaying Number of Local SGD Steps | University of Exeter | TPDS | 2023 | [PUB] [PDF] [CODE] |
| DRFL: Federated Learning in Diabetic Retinopathy Grading Using Fundus Images | National Institute of Technology Silchar | TPDS | 2023 | [PUB] |
| FedProf: Selective Federated Learning Based on Distributional Representation Profiling | Peng Cheng Laboratory | TPDS | 2023 | [PUB] [PDF] [UC] |
| Federated Ensemble Model-Based Reinforcement Learning in Edge Computing | University of Exeter | TPDS | 2023 | [PUB] [PDF] |
| Incentive Mechanism Design for Joint Resource Allocation in Blockchain-Based Federated Learning. | IUPUI | TPDS | 2023 | [PUB] [PDF] |
| HiFlash: Communication-Efficient Hierarchical Federated Learning With Adaptive Staleness Control and Heterogeneity-Aware Client-Edge Association. | SYSU | TPDS | 2023 | [PUB] [PDF] |
| From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization. | PolyU | TPDS | 2023 | [PUB] [PDF] [CODE] |
| Federated Learning Over Coupled Graphs | XJTU | TPDS | 2023 | [PUB] [PDF] |
| Privacy vs. Efficiency: Achieving Both Through Adaptive Hierarchical Federated Learning | NUDT | TPDS | 2023 | [PUB] |
| On Model Transmission Strategies in Federated Learning With Lossy Communications | SZU | TPDS | 2023 | [PUB] |
| Scheduling Algorithms for Federated Learning With Minimal Energy Consumption | University of Bordeaux | TPDS | 2023 | [PUB] [PDF] [CODE] |
| Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems | MEMUKUL | TPDS | 2023 | [PUB] [PDF] |
| Personalized Edge Intelligence via Federated Self-Knowledge Distillation. | HUST | TPDS | 2023 | [PUB] [CODE] |
| Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning. | MEMUKUL | TPDS | 2023 | [PUB] |
| Multi-Job Intelligent Scheduling With Cross-Device Federated Learning. | Baidu | TPDS | 2023 | [PUB] [PDF] |
| Data-Centric Client Selection for Federated Learning Over Distributed Edge Networks. | IIT | TPDS | 2023 | [PUB] |
| GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication. | HKBU | TPDS | 2023 | [PUB] |
| FedMDS: An Efficient Model Discrepancy-Aware Semi-Asynchronous Clustered Federated Learning Framework. | CQU | TPDS | 2023 | [PUB] |
| HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing. | DUT | TPDS | 2023 | [PUB] |
| Data selection for efficient model update in federated learning | EuroSys workshop | 2022 | [PUB] | |
| Empirical analysis of federated learning in heterogeneous environments | EuroSys workshop | 2022 | [PUB] | |
| BAFL: A Blockchain-Based Asynchronous Federated Learning Framework | karena | 2022 | [PUB] [CODE] | |
| L4L: Experience-Driven Computational Resource Control in Federated Learning | karena | 2022 | [PUB] | |
| Adaptive Federated Learning on Non-IID Data With Resource Constraint | karena | 2022 | [PUB] | |
| Locking Protocols for Parallel Real-Time Tasks With Semaphores Under Federated Scheduling. | TCAD | 2022 | [PUB] | |
| Client Scheduling and Resource Management for Efficient Training in Heterogeneous IoT-Edge Federated Learning | ECNU | TCAD | 2022 | [PUB] |
| PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT Systems. | ECNU | TCAD | 2022 | [PUB] |
| FHDnn: communication efficient and robust federated learning for AIoT networks | UC San Diego | DAC | 2022 | [PUB] |
| A Decentralized Federated Learning Framework via Committee Mechanism With Convergence Guarantee | SYSU | TPDS | 2022 | [PUB] [PDF] |
| Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted Model Aggregation | KAMIS | TPDS | 2022 | [PUB] |
| $f$funcX: Federated Function as a Service for Science. | SUST | TPDS | 2022 | [PUB] [PDF] |
| Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation | NUST | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Adaptive Federated Deep Reinforcement Learning for Proactive Content Caching in Edge Computing. | CQU | TPDS | 2022 | [PUB] |
| TDFL: Truth Discovery Based Byzantine Robust Federated Learning | SEDIKIT | TPDS | 2022 | [PUB] |
| Federated Learning With Nesterov Accelerated Gradient | The University of Sydney | TPDS | 2022 | [PUB] [PDF] |
| FedGraph: Federated Graph Learning with Intelligent Sampling | UoA | TPDS | 2022 | [PUB] [CODE] [解读] |
| AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning. | KAMIS | TPDS | 2022 | [PUB] |
| DONE: Distributed Approximate Newton-type Method for Federated Edge Learning. | University of Sydney | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Flexible Clustered Federated Learning for Client-Level Data Distribution Shift. | CQU | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks. | Xidian University | TPDS | 2022 | [PUB] |
| LightFed: An Efficient and Secure Federated Edge Learning System on Model Splitting. | CSU | TPDS | 2022 | [PUB] |
| On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. | Purdue | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Incentive-Aware Autonomous Client Participation in Federated Learning. | Sun Yat-sen University | TPDS | 2022 | [PUB] |
| Communicational and Computational Efficient Federated Domain Adaptation. | HKUST | TPDS | 2022 | [PUB] |
| Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning. | NTU | TPDS | 2022 | [PUB] |
| Differentially Private Byzantine-Robust Federated Learning. | Qufu Normal University | TPDS | 2022 | [PUB] |
| Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing. | University of Exeter | TPDS | 2022 | [PUB] [PDF] [CODE] |
| Reputation-Aware Hedonic Coalition Formation for Efficient Serverless Hierarchical Federated Learning. | BUAA | TPDS | 2022 | [PUB] |
| Differentially Private Federated Temporal Difference Learning. | Stony Brook University | TPDS | 2022 | [PUB] |
| Towards Efficient and Stable K-Asynchronous Federated Learning With Unbounded Stale Gradients on Non-IID Data. | XJTU | TPDS | 2022 | [PUB] [PDF] |
| Communication-Efficient Federated Learning With Compensated Overlap-FedAvg. | SCU | TPDS | 2022 | [PUB] [PDF] [CODE] |
| PAPAYA: Practical, Private, and Scalable Federated Learning. | Meta AI | MLSys | 2022 | [PUB] [PDF] |
| LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning | USC | MLSys | 2022 | [PUB] [PDF] [CODE] |
| Accelerated Training via Device Similarity in Federated Learning | EuroSys workshop | 2021 | [PUB] | |
| Towards Federated Learning with Attention Transfer to Mitigate System and Data Heterogeneity of Clients | EuroSys workshop | 2021 | [PUB] | |
| Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization | EuroSys workshop | 2021 | [PUB] | |
| SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead | University of Warwick | karena | 2021 | [PDF] [PUB] [CODE] |
| Efficient Federated Learning for Cloud-Based AIoT Applications | ECNU | TCAD | 2021 | [PUB] |
| HADFL: Heterogeneity-aware Decentralized Federated Learning Framework | USTC | DAC | 2021 | [PDF] [PUB] |
| Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration. | GMU | DAC | 2021 | [PDF] [PUB] |
| FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control. | ECNU | DAC | 2021 | [PUB] |
| Oort: Efficient Federated Learning via Guided Participant Selection | University of Michigan | OSDI | 2021 | [PUB] [PDF] [CODE] [SLIDES] [VIDEO] |
| Towards Efficient Scheduling of Federated Mobile Devices Under Computational and Statistical Heterogeneity. | Old Dominion University | TPDS | 2021 | [PUB] [PDF] |
| Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems. | CQU | TPDS | 2021 | [PUB] [CODE] |
| An Efficiency-Boosting Client Selection Scheme for Federated Learning With Fairness Guarantee | BANGSAT | TPDS | 2021 | [PUB] [PDF] [解读] |
| Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm. | Beijing Normal University | TPDS | 2021 | [PUB] [PDF] |
| Biscotti: A Blockchain System for Private and Secure Federated Learning. | UBC | TPDS | 2021 | [PUB] |
| Mutual Information Driven Federated Learning. | Deakin University | TPDS | 2021 | [PUB] |
| Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile Edge Computing Systems. | University of Warwick | TPDS | 2021 | [PUB] [PDF] |
| FedSCR: Structure-Based Communication Reduction for Federated Learning. | HKU | TPDS | 2021 | [PUB] |
| FedScale: Benchmarking Model and System Performance of Federated Learning | University of Michigan | SOSP workshop / ICML 2022 | 2021 | [PUB] [PDF] [CODE] [解读] |
| Redundancy in cost functions for Byzantine fault-tolerant federated learning | SOSP workshop | 2021 | [PUB] | |
| Towards an Efficient System for Differentially-private, Cross-device Federated Learning | SOSP workshop | 2021 | [PUB] | |
| GradSec: a TEE-based Scheme Against Federated Learning Inference Attacks | SOSP workshop | 2021 | [PUB] | |
| Community-Structured Decentralized Learning for Resilient EI. | SOSP workshop | 2021 | [PUB] | |
| Separation of Powers in Federated Learning (Poster Paper) | IBM Research | SOSP workshop | 2021 | [PUB] [PDF] |
| Towards federated unsupervised representation learning | EuroSys workshop | 2020 | [PUB] | |
| CoLearn: enabling federated learning in MUD-compliant IoT edge networks | EuroSys workshop | 2020 | [PUB] | |
| LDP-Fed: federated learning with local differential privacy. | EuroSys workshop | 2020 | [PUB] | |
| Accelerating Federated Learning via Momentum Gradient Descent. | USTC | TPDS | 2020 | [PUB] [PDF] |
| Towards Fair and Privacy-Preserving Federated Deep Models. | NUS | TPDS | 2020 | [PUB] [PDF] [CODE] |
| Federated Optimization in Heterogeneous Networks | CMU | MLSys | 2020 | [PUB] [PDF] [CODE] |
| Towards Federated Learning at Scale: System Design | MLSys | 2019 | [PUB] [PDF] [解读] |
Federated Learning papers accepted by top conference and journal in the other fields, including ICSE(International Conference on Software Engineering), FOCS(IEEE Annual Symposium on Foundations of Computer Science), STOC(Symposium on the Theory of Computing).
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| F-CodeLLM: A Federated Learning Framework for Adapting Large Language Models to Practical Software Development | SYSU | ICSE Companion | 2024 | PUB |
| Raft Protocol for Fault Tolerance and Self-Recovery in Federated Learning | SINTEF Digital | SEAMS@ICSE | 2024 | PUB |
| FedDebug: Systematic Debugging for Federated Learning Applications. | Virginia Tech | ICSE | 2023 | pub pdf code |
| FedSlice: Protecting Federated Learning Models from Malicious Participants with Model Slicing. | PKU | ICSE | 2023 | pub code |
| Towards a Self-Adaptive Architecture for Federated Learning of Industrial Automation Systems | SEAMS@ICSE workshop | 2021 | pub | |
| Federated Machine Learning as a Self-Adaptive Problem | SEAMS@ICSE workshop | 2021 | pub |
This section partially refers to DBLP search engine and repositories Awesome-Federated-Learning-on-Graph-and-GNN-papers and Awesome-Federated-Machine-Learning.
| Judul | Afiliasi | Lokasi | Tahun | Bahan |
|---|---|---|---|---|
| FedGCN: Convergence and Communication Tradeoffs in Federated Training of Graph Convolutional Networks | CMU | NeurIPS ? | 2023 | [PDF] [CODE] |
| Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking | CMU | NeurIPS Dataset Track ? | 2023 | [PDF] [DATASET] [CODE] |
| Federated Visualization: A Privacy-Preserving Strategy for Aggregated Visual Query. | ZJU | IEEE Trans. Vis. Comput. Grafik. ? | 2023 | [PUB] [PDF] |
| Personalized Subgraph Federated Learning | KAIST | ICML ? | 2023 | [PDF] |
| Semi-decentralized Federated Ego Graph Learning for Recommendation | SUST | WWW:mortar_board: | 2023 | [PUB] [PDF] |
| Federated Graph Neural Network for Fast Anomaly Detection in Controller Area Networks | ECUST | IEEE Trans. Inf. Forensics Secur. ? | 2023 | [PUB] |
| Federated Learning Over Coupled Graphs | XJTU | IEEE Trans. Parallel Distributed Syst. ? | 2023 | [PUB] [PDF] |
| HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning | Nankai University | IEEE Trans. Vis. Comput. Grafik. ? | 2023 | [PUB] [PDF] |
| Federated Learning on Non-IID Graphs via Structural Knowledge Sharing | UTS | AAAI ? | 2023 | [PDF] [CODE] |
| FedGS: Federated Graph-based Sampling with Arbitrary Client Availability | XMU | AAAI ? | 2023 | [PDF] [CODE] |
| An Information Theoretic Perspective for Heterogeneous Subgraph Federated Learning. | PKU | DASFAA | 2023 | [PUB] |
| GraphCS: Graph-based client selection for heterogeneity in federated learning | NUDT | J. Parallel Distributed Comput. | 2023 | [PUB] |
| Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach | BUPT | IEEE Trans. Neural Networks Learn. Syst. | 2023 | [PUB] [PDF] |
| Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks and Federated Learning | ZUEL | IEEE Trans. Intell. Transp. Syst. | 2023 | [PUB] |
| Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection. | HVL | IEEE J. Biomed. Health Informatics | 2023 | [PUB] |
| Federated Learning-Based Cross-Enterprise Recommendation With Graph Neural | IEEE Trans. Ind. Informatics | 2023 | [PUB] | |
| Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning | ZJUT | IEEE Trans. Comput. Soc. Syst. | 2023 | [PUB] [PDF] [CODE] |
| ESA-FedGNN: Efficient secure aggregation for federated graph neural networks. | Peer Peer Netw. Aplikasi. | 2023 | [PUB] | |
| FedCKE: Cross-Domain Knowledge Graph Embedding in Federated Learning | SWJTU | IEEE Trans. Data Besar | 2023 | [PUB] |
| Asynchronous federated learning with directed acyclic graph-based blockchain in edge computing: Overview, design, and challenges. | Expert Syst. Aplikasi. | 2023 | [PUB] | |
| FedGR: Federated Graph Neural Network for Recommendation System | CUPT | Axioms | 2023 | [PUB] |
| S-Glint: Secure Federated Graph Learning With Traffic Throttling and Flow Scheduling. | IEEE Trans. Green Commun. Netw. | 2023 | [PUB] | |
| FedAGCN: A traffic flow prediction framework based on federated learning and Asynchronous Graph Convolutional Network | Aplikasi. Soft Comput. | 2023 | [PUB] | |
| GDFed: Dynamic Federated Learning for Heterogenous Device Using Graph Neural Network | KHU | ICOIN | 2023 | [PUB] [CODE] |
| Coordinated Scheduling and Decentralized Federated Learning Using Conflict Clustering Graphs in Fog-Assisted IoD Networks | UBC | IEEE Trans. Veh. Technol. | 2023 | [PUB] |
| FedRule: Federated Rule Recommendation System with Graph Neural Networks | CMU | IoTDI | 2023 | [PUB] [PDF] [CODE] |
| FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy | SJTU | KDD ? | 2022 | [PUB] [PDF] |
| FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning | Alibaba | KDD (Best Paper Award) ? | 2022 | [PDF] [CODE] [PUB] |
| Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning | SJTU | ICML ? | 2022 | [PUB] [CODE] |
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting kg. | ZJU | IJCAI ? | 2022 | [PUB] [PDF] [CODE] |
| Personalized Federated Learning With a Graph | UTS | IJCAI ? | 2022 | [PUB] [PDF] [CODE] |
| Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification | ZJU | IJCAI ? | 2022 | [PUB] [PDF] |
| SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data | USC | AAAI:mortar_board: | 2022 | [PUB] [PDF] [CODE] [解读] |
| FedGraph: Federated Graph Learning with Intelligent Sampling | UoA | TPDS ? | 2022 | [PUB] [CODE] [解读] |
Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications surv. | University of Virginia | SIGKDD Explor. | 2022 | [PUB] [PDF] |
| Semantic Vectorization: Text- and Graph-Based Models. | IBM Research | Federated Learning | 2022 | [PUB] |
| GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs | IIT | ICDM | 2022 | [PUB] [PDF] [解读] |
| More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks | TU Delft | ACSAC | 2022 | [PUB] [PDF] |
| FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction | UESTC | TMI | 2022 | [PUB] [PDF] |
| SemiGraphFL: Semi-supervised Graph Federated Learning for Graph Classification. | PKU | PPSN | 2022 | [PUB] |
| Federated Spatio-Temporal Traffic Flow Prediction Based on Graph Convolutional Network | TJU | WCSP | 2022 | [PUB] |
| A federated graph neural network framework for privacy-preserving personalization | KAMIS | Nature Communications | 2022 | [PUB] [CODE] [解读] |
| Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning | SEDIKIT | INFOCOM Workshops | 2022 | [PUB] |
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation kg. | Lehigh University | EMNLP | 2022 | [PUB] [PDF] [CODE] |
| Power Allocation for Wireless Federated Learning using Graph Neural Networks | Rice University | ICASSP | 2022 | [PUB] [PDF] [CODE] |
| Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization | universitas | ICASSP | 2022 | [PUB] [PDF] [CODE] |
| Graph-regularized federated learning with shareable side information | NWPU | Knowl. Based Syst. | 2022 | [PUB] |
Federated knowledge graph completion via embedding-contrastive learning kg. | ZJU | Knowl. Based Syst. | 2022 | [PUB] |
| Federated Graph Learning with Periodic Neighbour Sampling | HKU | IWQoS | 2022 | [PUB] |
| FedGSL: Federated Graph Structure Learning for Local Subgraph Augmentation. | Data Besar | 2022 | [PUB] | |
| Domain-Aware Federated Social Bot Detection with Multi-Relational Graph Neural Networks. | UCAS; KAS | IJCNN | 2022 | [PUB] |
| A Federated Multi-Server Knowledge Graph Embedding Framework For Link Prediction. | ICTAI | 2022 | [PUB] | |
| A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy | Ping An Technology | KSEM | 2022 | [PUB] [PDF] |
| Clustered Graph Federated Personalized Learning. | NTNU | IEEECONF | 2022 | [PUB] |
| Investigating the Predictive Reproducibility of Federated Graph Neural Networks using Medical Datasets. | MICCAI Workshop | 2022 | [PDF] [CODE] | |
| Peer-to-Peer Variational Federated Learning Over Arbitrary Graphs | UCSD | Int. J. Bio Inspired |
