جدول المحتويات
نحن نستخدم مشروعًا آخر لتتبع التحديثات لأوراق FL تلقائيًا، انقر فوق FL-paper-update-tracker إذا كنت في حاجة إليها.
سيتم إضافة المزيد من العناصر إلى المستودع . لا تتردد في اقتراح موارد رئيسية أخرى عن طريق فتح تقرير مشكلة، أو إرسال طلب سحب، أو إرسال بريد إلكتروني إليّ @ ([email protected]). إذا كنت ترغب في التواصل مع المزيد من الأصدقاء في مجال التعلم الموحد، يرجى الانضمام إلى مجموعة QQ [联邦学习交流群]، رقم المجموعة هو 833638275. استمتع بالقراءة!
إشعار تحديث المستودع
2024/09/30
أعزائي المستخدمين، نود أن نعلمكم ببعض التغييرات التي ستؤثر على هذا المستودع مفتوح المصدر. المالك والمساهم الرئيسي @youngfish42 أكمل بنجاح دراسة الدكتوراه؟ اعتبارًا من 30 سبتمبر 2024، ومنذ ذلك الحين قام بتحويل تركيز بحثه. سيؤثر هذا التغيير في الظروف على تكرار ومدى التحديثات لقائمة الأوراق الخاصة بالمستودع.
بدلاً من التحديثات المنتظمة السابقة، نتوقع أن يتم الآن تحديث القائمة الورقية على أساس شهري أو ربع سنوي. علاوة على ذلك، سيتم تقليل عمق هذه التحديثات. على سبيل المثال، لن يتم الاحتفاظ بالتحديثات المتعلقة بمؤسسة المؤلف والتعليمات البرمجية مفتوحة المصدر بشكل نشط.
نحن ندرك أن هذا قد يؤثر على القيمة التي تستمدها من هذا المستودع. ولذلك، فإننا بكل تواضع ندعو المزيد من المساهمين للمشاركة في تحديث المحتوى. سيضمن هذا الجهد التعاوني أن يظل المستودع موردًا قيمًا للجميع.
نحن نقدر تفهمك ونتطلع إلى دعمك ومساهماتك المستمرة.
أطيب التحيات،
白小鱼 (سمكة صغيرة)
فئات
الذكاء الاصطناعي (IJCAI، AAAI، AISTATS، ALT، AI)
التعلم الآلي (NeurIPS، ICML، ICLR، COLT، UAI، التعلم الآلي، JMLR، TPAMI)
استخراج البيانات (KDD، WSDM)
آمن (S&P، CCS، USENIX Security، NDSS)
الرؤية الحاسوبية (ICCV، CVPR، ECCV، MM، IJCV)
معالجة اللغات الطبيعية (ACL، EMNLP، NAACL، COLING)
استرجاع المعلومات (SIGIR)
قاعدة البيانات (SIGMOD، ICDE، VLDB)
الشبكة (SIGCOMM، INFOCOM، MOBICOM، NSDI، WWW)
النظام (OSDI، SOSP، ISCA، MLSys، EuroSys، TPDS، DAC، TOCS، TOS، TCAD، TC)
أخرى (ICSE، FOCS، STOC)
مكان | 2024-2020 | قبل عام 2020 |
---|---|---|
IJCAI | 24، 23، 22، 21، 20 | 19 |
AAAI | 24، 23، 22، 21، 20 | - |
أستاتس | 24، 23، 22، 21، 20 | - |
بديل | 22 | - |
منظمة العفو الدولية (ي) | 23 | - |
نوريبس | 24، 23، 22، 21، 20 | 18، 17 |
آي سي إم إل | 24، 23، 22، 21، 20 | 19 |
ICLR | 24، 23، 22، 21، 20 | - |
كولت | 23 | - |
UAI | 23، 22، 21 | - |
التعلم الآلي (ي) | 24، 23، 22 | - |
جملر (ي) | 24، 23، 22 | - |
تبامي (ي) | 25، 24، 23، 22 | - |
كي دي دي | 24، 23، 22، 21، 20 | |
WSDM | 24، 23، 22، 21 | 19 |
ستاندرد آند بورز | 24، 23، 22 | 19 |
احتجاز ثاني أكسيد الكربون | 24، 23، 22، 21، 19 | 17 |
أمن يوزينيكس | 23، 22، 20 | - |
NDSS | 24، 23، 22، 21 | - |
CVPR | 24، 23، 22، 21 | - |
ICCV | 23,21 | - |
إكف | 24، 22، 20 | - |
مم | 24، 23، 22، 21، 20 | - |
إيجكف (ي) | 24 | - |
الرباط الصليبي الأمامي | 23، 22، 21 | 19 |
NAACL | 24، 22، 21 | - |
EMNLP | 24، 23، 22، 21، 20 | - |
كولينج | 20 | - |
سيجير | 24، 23، 22، 21، 20 | - |
سيجمود | 22، 21 | - |
ICDE | 24، 23، 22، 21 | - |
فلدب | 23، 22، 21، 21، 20 | - |
سيجكوم | - | - |
معلومات | 24، 23، 22، 21، 20 | 19، 18 |
موبيكوم | 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 |
يوروسيس | 24، 23، 22، 21، 20 | |
TPDS (ي) | 24، 23، 22، 21، 20 | - |
لجنة المساعدة الإنمائية | 24، 22، 21 | - |
TOCS | - | - |
شروط الخدمة | - | - |
تكاد | 24، 23، 22، 21 | - |
ح | 24، 23، 22، 21 | - |
ICSE | 23، 21 | - |
FOCS | - | - |
ستوك | - | - |
الكلمات الرئيسية
الإحصائيات: الكود متاح والنجوم >= 100 | الاقتباس >= 50 | ؟ مكان من الدرجة الأولى
kg.
: الرسم البياني المعرفي | data.
: مجموعة البيانات | surv.
: استطلاع
تشير أوراق التعلم الموحد في Nature (ومجلاتها الفرعية)، وCell، وScience (وScience Advances)، وPANS إلى محرك بحث WOS.
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
MatSwarm: حساب المواد المعتمدة على تعلم نقل السرب الموثوق به لمشاركة البيانات الضخمة بشكل آمن | يو اس تي بي؛ NTU | نات. مشترك. | 2024 | [حانة] [الكود] |
تقديم ذكاء الحافة إلى العدادات الذكية من خلال التعلم المقسم الموحد | جامعة هونغ كونغ | نات. مشترك. | 2024 | [PUB] [النسخة] |
دراسة دولية تقدم منصة تعليمية موحدة للذكاء الاصطناعي لأورام الدماغ لدى الأطفال | جامعة ستانفورد | نات. مشترك. | 2024 | [حانة] [الكود] |
PPML-Omics: طريقة التعلم الآلي الموحدة التي تحافظ على الخصوصية تحمي خصوصية المرضى في بيانات omic | كاوست | تقدم العلوم | 2024 | [حانة] [الكود] |
التعلم الموحد ليس علاجًا شاملاً لأخلاقيات البيانات | توم؛ الأشعة فوق البنفسجية | نات. ماخ. إنتل.(تعليق) | 2024 | [حانة] |
نموذج تعليمي متحد قوي لتحديد المرضى المعرضين لمخاطر عالية والذين يعانون من تكرار الإصابة بسرطان المعدة بعد العملية الجراحية | مستشفى جيانغمن المركزي؛ جامعة قويلين لتكنولوجيا الفضاء الجوي؛ جامعة قويلين للتكنولوجيا الإلكترونية؛ | نات. مشترك. | 2024 | [حانة] [الكود] |
تبادل المعرفة الانتقائية للتقطير الاتحادي للحفاظ على الخصوصية دون معلم جيد | HKUST | نات. مشترك. | 2024 | [PUB] [PDF] [الكود] |
نظام تعليمي موحد لطب الأورام الدقيق في أوروبا: DigiONE | IQVIA لأبحاث السرطان BV | نات. ميد. (تعليق) | 2024 | [حانة] |
توزيع حساب كمي أعمى متعدد العملاء باستخدام بنية Qline | جامعة سابينزا دي روما | نات. مشترك. | 2023 | [PUB] [PDF] |
العشوائية الكمومية المستقلة عن الجهاز - دليل المعرفة الصفرية المعزز | USTC | بناس | 2023 | [PUB] [PDF] [النسخة الأصلية] |
فرز البطاريات المتقاعدة بطريقة تعاونية والحفاظ على الخصوصية لإعادة التدوير المباشر المربح عبر التعلم الآلي الموحد | جامعة تسينغهوا | نات. مشترك. | 2023 | [حانة] |
الدعوة لخصوصية البيانات العصبية وتنظيم التكنولوجيا العصبية | جامعة كولومبيا | نات. بروتوك. (وجهة نظر) | 2023 | [حانة] |
القياس الموحد للذكاء الاصطناعي الطبي مع MedPerf | آي إتش يو ستراسبورغ؛ جامعة ستراسبورغ؛ معهد دانا فاربر للسرطان؛ طب وايل كورنيل؛ كلية هارفارد تي تشان للصحة العامة؛ معهد ماساتشوستس للتكنولوجيا. إنتل | نات. ماخ. إنتل. | 2023 | [PUB] [PDF] [الكود] |
العدالة الخوارزمية في الذكاء الاصطناعي للطب والرعاية الصحية | كلية الطب بجامعة هارفارد؛ المعهد الواسع لمعهد هارفارد وماساتشوستس للتكنولوجيا؛ معهد دانا فاربر للسرطان | نات. بيوميد. م. (وجهة نظر) | 2023 | [PUB] [PDF] |
نقل المعرفة الخاصة بشكل تفاضلي للتعلم الموحد | الخميس | نات. مشترك. | 2023 | [حانة] [الكود] |
التعلم الموحد اللامركزي من خلال مشاركة نموذج الوكيل | الطبقة السادسة للذكاء الاصطناعي؛ جامعة واترلو؛ معهد المتجهات | نات. مشترك. | 2023 | [PUB] [PDF] [الكود] |
التعلم الآلي الموحد في الأبحاث المتوافقة مع حماية البيانات | جامعة هامبورغ | نات. ماخ. إنتل.(تعليق) | 2023 | [حانة] |
التعلم الموحد للتنبؤ بالاستجابة النسيجية للعلاج الكيميائي المساعد الجديد في سرطان الثدي الثلاثي السلبي | أوكين | نات. ميد. | 2023 | [حانة] [الكود] |
يتيح التعلم الموحد البيانات الضخمة للكشف عن حدود السرطان النادر | جامعة بنسلفانيا | نات. مشترك. | 2022 | [PUB] [PDF] [الكود] |
التعلم الموحد وسيادة البيانات الجينومية الأصلية | تعانق الوجه | نات. ماخ. إنتل. (تعليق) | 2022 | [حانة] |
التعلم التمثيلي المفكك الموحد للكشف عن شذوذ الدماغ دون إشراف | توم | نات. ماخ. إنتل. | 2022 | [PUB] [PDF] [الكود] |
تحويل التعلم الآلي للرعاية الصحية من التطوير إلى النشر ومن النماذج إلى البيانات | جامعة ستانفورد؛ جرينستون للعلوم البيولوجية | نات. بيوميد. م. (مقالة مراجعة) | 2022 | [حانة] |
إطار عمل للشبكة العصبية ذات الرسم البياني الموحد لتخصيص الحفاظ على الخصوصية | الخميس | نات. مشترك. | 2022 | [PUB] [الكود] [الترجمة] |
التعلم الموحد الفعال في مجال الاتصالات من خلال تقطير المعرفة | الخميس | نات. مشترك. | 2022 | [PUB] [PDF] [الكود] |
قيادة التعلم العصبي الموحد للذكاء الاصطناعي للحافة اللاسلكية | XMU؛ NTU | نات. مشترك. | 2022 | [PUB] [الكود] [الترجمة] |
نهج تعليمي اتحادي لامركزي جديد للتدريب على البيانات الطبية الخاصة الموزعة عالميًا وذات الجودة الرديئة والمحمية | جامعة ولونجونج | الخيال العلمي. مندوب. | 2022 | [حانة] |
تطوير تشخيص فيروس كورونا (COVID-19) من خلال التعاون في الحفاظ على الخصوصية في مجال الذكاء الاصطناعي | هوست | نات. ماخ. إنتل. | 2021 | [PUB] [PDF] [الكود] |
التعلم الموحد للتنبؤ بالنتائج السريرية لدى المرضى المصابين بـCOVID-19 | MGH الأشعة وكلية الطب بجامعة هارفارد | نات. ميد. | 2021 | [حانة] [الكود] |
التدخل العدائي وتخفيفه في التعلم الآلي التعاوني الذي يحافظ على الخصوصية | إمبريال كوليدج لندن؛ توم؛ أوبنميند | نات. ماخ. إنتل. (المنظور) | 2021 | [حانة] |
Swarm Learning للتعلم الآلي السريري اللامركزي والسري | دزني؛ جامعة بون؛ | طبيعة ؟ | 2021 | [PUB] [الكود] [البرنامج] [الترجمة] |
خصوصية شاملة تحافظ على التعلم العميق في التصوير الطبي متعدد المؤسسات | توم؛ إمبريال كوليدج لندن؛ أوبنميند | نات. ماخ. إنتل. | 2021 | [PUB] [الكود] [الترجمة] |
التعلم الموحد الفعال في التواصل | CUHK؛ جامعة برينستون | المقالي. | 2021 | [حانة] [الكود] |
كسر حدود مشاركة البيانات الطبية باستخدام الصور الشعاعية المركبة | جامعة RWTH آخن | علوم. السلف. | 2020 | [حانة] [الكود] |
التعلم الآلي الآمن والمحافظ على الخصوصية والموحد في التصوير الطبي | توم؛ إمبريال كوليدج لندن؛ أوبنميند | نات. ماخ. إنتل. (المنظور) | 2020 | [حانة] |
أوراق التعلم الموحد المقبولة من قبل أهم مؤتمرات ومجلات الذكاء الاصطناعي، بما في ذلك IJCAI (المؤتمر الدولي المشترك حول الذكاء الاصطناعي)، AAAI (مؤتمر AAAI حول الذكاء الاصطناعي)، AISTATS (الذكاء الاصطناعي والإحصاء)، ALT (المؤتمر الدولي لتعلم الخوارزميات) النظرية)، الذكاء الاصطناعي (الذكاء الاصطناعي).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
تجميع متعدد العرض الموحد عبر تحليل الموتر | IJCAI | 2024 | [حانة] | |
تجميع متعدد العروض متحد فعال مع تحليل المصفوفة المتكامل وK-Means | IJCAI | 2024 | [حانة] | |
LG-FGAD: إطار عمل فعال للكشف عن شذوذ الرسم البياني الموحد | IJCAI | 2024 | [حانة] | |
التعلم الفوري الموحد لنماذج مؤسسة الطقس على الأجهزة | IJCAI | 2024 | [حانة] | |
كسر حواجز عدم تجانس النظام: التعلم الموحد متعدد الوسائط المتسامح مع المتطرفين من خلال تقطير المعرفة | IJCAI | 2024 | [حانة] | |
التخلص من التعلم أثناء التعلم: طريقة فعالة للتعلم من خلال الآلة الموحدة | IJCAI | 2024 | [حانة] | |
ضغط متدرج هجين عملي لأنظمة التعلم الموحدة | IJCAI | 2024 | [حانة] | |
اكتشاف السببية الموحدة المدركة لعدم تجانس جودة العينة من خلال اختيار الفضاء المتغير التكيفي | IJCAI | 2024 | [حانة] [الكود] | |
ميزة التعلم الموحد المنظم: الاستفادة من تباينات البيانات لتحقيق مكاسب في أداء النموذج | IJCAI | 2024 | [حانة] [الكود] | |
القياس الكمي لعدم اليقين القائم على ديريشليت للتعلم الموحد المخصص مع الشبكات الخلفية المحسنة | IJCAI | 2024 | [حانة] | |
FedConPE: قطاع طرق محادثة اتحادي فعال مع عملاء غير متجانسين | IJCAI | 2024 | [حانة] | |
DarkFed: هجوم خلفي خالٍ من البيانات في التعلم الموحد | IJCAI | 2024 | [حانة] | |
التعلم الموحد القابل للتطوير من خلال المشاركة المعزولة والمشفرة | IJCAI | 2024 | [حانة] | |
تعزيز التوصيات عبر المجالات ذات الهدف المزدوج من خلال التعلم الموحد للحفاظ على الخصوصية | IJCAI | 2024 | [حانة] | |
تسرب الملصقات في التعلم الموحد العمودي: دراسة استقصائية | IJCAI | 2024 | [حانة] | |
صعود الذكاء الفيدرالي: من نماذج المؤسسات الفيدرالية نحو الذكاء الجماعي | IJCAI | 2024 | [حانة] | |
LEAP: تحسين التعلم الموحد الهرمي على البيانات غير IID باستخدام لعبة تشكيل التحالف | IJCAI | 2024 | [حانة] | |
EAB-FL: تفاقم التحيز الخوارزمي من خلال هجمات التسمم النموذجية في التعلم الموحد | IJCAI | 2024 | [حانة] | |
تقطير المعرفة في التعلم الموحد: دليل عملي | IJCAI | 2024 | [حانة] | |
FedGCS: إطار عمل إبداعي لاختيار العملاء بكفاءة في التعلم الموحد من خلال التحسين القائم على التدرج | IJCAI | 2024 | [حانة] | |
FedPFT: الضبط الدقيق للنماذج الأساسية للوكيل الموحد | IJCAI | 2024 | [حانة] [الكود] | |
مسح منهجي للتعلم الموحد شبه الخاضع للإشراف | IJCAI | 2024 | [حانة] | |
الوكلاء الأذكياء للتعلم الموحد القائم على المزادات: دراسة استقصائية | IJCAI | 2024 | [حانة] | |
استراتيجية عروض الأسعار الخالية من التحيز والتي تعمل على تعظيم الإيرادات لمستهلكي البيانات في التعلم الموحد القائم على المزادات | IJCAI | 2024 | [حانة] | |
التعلم الموحد المخصص القائم على المعايرة المزدوجة | IJCAI | 2024 | [حانة] | |
دعم القرار الموجه لأصحاب المصلحة للتعلم الموحد القائم على المزاد | IJCAI | 2024 | [حانة] | |
إعادة تعريف المساهمات: التعلم الموحد القائم على شابلي | IJCAI | 2024 | [حانة] [الكود] | |
استبيان حول أساليب التعلم الموحدة الفعالة للتدريب النموذجي التأسيسي | IJCAI | 2024 | [حانة] | |
من التحسين إلى التعميم: التعلم الموحد العادل مقابل تحول الجودة من خلال مطابقة الحدة بين العملاء | IJCAI | 2024 | [حانة] [الكود] | |
FBLG: نهج قائم على الرسم البياني المحلي للتعامل مع البيانات المزدوجة المنحرفة غير IID في التعلم الموحد | IJCAI | 2024 | [حانة] | |
FedFa: نموذج تدريب غير متزامن تمامًا للتعلم الموحد | IJCAI | 2024 | [حانة] | |
FedSSA: التجميع الدلالي القائم على التشابه من أجل التعلم الموحد المخصص للنموذج غير المتجانس | IJCAI | 2024 | [حانة] | |
FedES: الإيقاف المبكر الموحد لإعاقة حفظ ضوضاء التسمية غير المتجانسة | IJCAI | 2024 | [حانة] | |
التعلم الموحد المخصص للتنبؤ بحركة المرور عبر المدن | IJCAI | 2024 | [حانة] | |
التكيف الموحد للتوصيات المستندة إلى نموذج الأساس | IJCAI | 2024 | [حانة] | |
BADFSS: الهجمات الخلفية على التعلم الفيدرالي الخاضع للإشراف الذاتي | IJCAI | 2024 | [حانة] | |
التقدير قبل التحيز: نهج بايزي لفصل التحيز المسبق في التعلم الموحد شبه الخاضع للإشراف | IJCAI | 2024 | [حانة] [الكود] | |
FedTAD: تقطير المعرفة الخالي من البيانات المدرك للطوبولوجيا للتعلم الموحد للرسم البياني الفرعي | IJCAI | 2024 | [حانة] | |
بوبا: التعلم البيزنطي القوي مع انحراف التسمية | UIUC | أستاتس | 2024 | [PUB] [PDF] [الكود] |
قطاع الطرق الخطي المتحد مع عملاء غير متجانسين | جامعة فرجينيا | أستاتس | 2024 | [PUB] [PDF] [الكود] |
تصميم التجربة الموحدة في ظل الخصوصية التفاضلية الموزعة | جامعة ستانفورد؛ ميتا | أستاتس | 2024 | [PUB] [PDF] [الكود] |
الهروب من نقاط السرج في التعلم الموحد غير المتجانس عبر SGD الموزع مع ضغط الاتصالات | جامعة برينستون | أستاتس | 2024 | [PUB] [PDF] |
SGD غير المتزامن على الرسوم البيانية: إطار عمل موحد للتحسين غير المتزامن اللامركزي والموحد | إنريا | أستاتس | 2024 | [PUB] [PDF] |
SIFU: محو التعلم الموحد المستنير المتسلسل من أجل محو التعلم الموحد للعميل بكفاءة وإثبات في التحسين الموحد | إنريا | أستاتس | 2024 | [PUB] [PDF] [الكود] |
الضغط مع توزيع الأخطاء الدقيق للتعلم الموحد | مدرسة البوليتكنيك | أستاتس | 2024 | [PUB] [PDF] [الكود] |
تحسين Minimax الموحد المتكيف مع تعقيدات أقل | جامعة نيو جيرسي؛ مختبر MIIT الرئيسي لتحليل الأنماط والذكاء الآلي | أستاتس | 2024 | [PUB] [PDF] |
الضغط التكيفي في التعلم الموحد عبر المعلومات الجانبية | جامعة ستانفورد؛ جامعة بادوفا | أستاتس | 2024 | [PUB] [PDF] [الكود] |
التعلم الموحد عند الطلب للتوزيعات التعسفية للفئات المستهدفة | UNIST | أستاتس | 2024 | [حانة] [الكود] |
FedFisher: الاستفادة من معلومات Fisher للتعلم الموحد بلقطة واحدة | جامعة كارنيجي ميلون | أستاتس | 2024 | [PUB] [PDF] [الكود] |
ديناميكيات قائمة الانتظار للتعلم الموحد غير المتزامن | هواوي | أستاتس | 2024 | [PUB] [PDF] |
قطاع الطرق المتحد ذو الذراع X المخصص | جامعة بوردو | أستاتس | 2024 | [PUB] [PDF] [الكود] |
التعلم الموحد للسجلات الصحية الإلكترونية غير المتجانسة باستخدام شبكات الاهتمام للرسم البياني الزمني المعزز | جامعة أكسفورد | أستاتس | 2024 | [حانة] [الكود] |
صعود التدرج العشوائي السلس لتحسين الحد الأدنى الموحد | جامعة فرجينيا | أستاتس | 2024 | [PUB] [PDF] |
فهم تعميم التعلم الموحد عبر الاستقرار: مسائل عدم التجانس | جامعة نورث وسترن | أستاتس | 2024 | [PUB] [PDF] [الكود] |
فوائد متبادلة يمكن إثباتها من التعلم الموحد في المجالات الحساسة للخصوصية | جامعة صوفيا | أستاتس | 2024 | [PUB] [PDF] [الكود] |
تحليل تسرب الخصوصية في نماذج اللغات الكبيرة الموحدة | جامعة فلوريدا | أستاتس | 2024 | [PUB] [PDF] [الكود] |
مجمع ثابت للدفاع ضد هجمات الباب الخلفي الفيدرالية | UIUC | أستاتس | 2024 | [PUB] [PDF] [الكود] |
التعلم الموحد ذو كفاءة الاتصال مع البيانات وعدم تجانس العملاء | ISTA | أستاتس | 2024 | [PUB] [PDF] [الكود] |
FedMut: التعلم الموحد المعمم عبر الطفرة العشوائية | NTU | AAAI | 2024 | [حانة] |
التعلم الجزئي الموحد مع التعزيز والتنظيم المحلي | جامعة كارلتون | AAAI | 2024 | [النشرة] [الصفحة] |
لا تحيز! الشبكات العصبية ذات الرسم البياني الموحد العادل للتوصية الشخصية | IIT | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
المنطق الرسمي يمكّن التعلم الموحد المخصص من خلال استنتاج الخاصية | جامعة فاندربيلت | AAAI | 2024 | [PUB] [PDF] |
التعلم التمثيلي الذي لا يلتزم بالخصوصية، ويحافظ على المهام، من أجل التعلم الموحد ضد هجمات استدلال السمات | إلينوي تك | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
التجارة العادلة: تحقيق مقايضات باريتو المثالية بين الدقة المتوازنة والعدالة في التعلم الموحد | جامعة لايبنتز | AAAI | 2024 | [النشرة] [الصفحة] |
مكافحة اختلالات البيانات في التعلم الموحد شبه الخاضع للإشراف باستخدام المنظمين المزدوجين | HKUST | AAAI | 2024 | [PUB] [صفحة] [PDF] |
Fed-QSSL: إطار عمل للتعلم الموحد المخصص في ظل عرض البت وعدم تجانس البيانات | يوتا | AAAI | 2024 | [PUB] [صفحة] [PDF] |
حول تفكيك نقل المعرفة غير المتماثل للتعلم المتحد المحايد لطريقة العمل | جامعة فرجينيا | AAAI | 2024 | [حانة] |
FedDAT: نهج لضبط النموذج الأساسي في التعلم الموحد غير المتجانس متعدد الوسائط | LMU ميونيخ سيمنز AG | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
انتبه لرأسك: تجميع رؤوس العرض للحفاظ على موثوقية النماذج الموحدة | مختبر شنشي الرئيسي المشترك لجامعة شيان جياوتونغ للذكاء الاصطناعي | AAAI | 2024 | [PUB] [صفحة] [PDF] |
FedGCR: تحقيق الأداء والعدالة للتعلم الموحد مع أنواع العملاء المتميزة من خلال تخصيص المجموعة وإعادة وزنها | NTU | AAAI | 2024 | [نشر] [صفحة] [رمز] |
أجهزة التشفير الموحدة الخاصة بالطرائق والمثبتات متعددة الوسائط لتجزئة أورام الدماغ الشخصية | جامعة شيامن | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
استغلال انحرافات التسمية في التعلم الموحد باستخدام تسلسل النماذج | جامعة سنغافورة الوطنية | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
استخلاص المعرفة التكميلية من أجل نموذج قوي يحافظ على الخصوصية ويخدم في التعلم الموحد الرأسي | جامعة العلوم والتكنولوجيا؛ HKUST | AAAI | 2024 | [النشرة] [الصفحة] |
التعلم الموحد عبر التقطير التعاوني للمدخلات والمخرجات | جامعة بوفالو؛ الولايات المتحدة الأمريكية كلية الطب بجامعة هارفارد | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
معايرة جولة واحدة من التعلم الموحد مع الاستدلال البايزي في الفضاء التنبؤي | معهد جامعة واترلو فيكتور | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
FedCSL: نهج دقيق وقابل للتطوير لتعلم البنية السببية الموحدة | HFUT | AAAI | 2024 | [PUB] [PDF] |
FedFixer: التخفيف من ضجيج التسمية غير المتجانسة في التعلم الموحد | جامعة شيان جياوتونغ؛ جامعة ليدن | AAAI | 2024 | [PUB] [صفحة] [PDF] |
FedLPS: التعلم الموحد غير المتجانس لمهام متعددة مع مشاركة المعلمات المحلية | جامعة نيو جيرسي | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
التعلم المتقارب المتحد ثلاثي المستوى | TJU | AAAI | 2024 | [PUB] [PDF] |
التعلم الموحد الأدائي: حل لتحولات التوزيع المعتمدة على النموذج وغير المتجانسة | أم | AAAI | 2024 | [النشرة] [الصفحة] |
الذكاء التجاري العام: المحرك الموحد محليًا القائم على البرمجة اللغوية العصبية (NLP) للحفاظ على الخصوصية والخدمات المخصصة المستدامة للتجار المتعددين | جامعة كيونغ هي؛ هاركس إنفوتيك | AAAI | 2024 | [النشرة] [الصفحة] |
EMGAN: Early-Mix-GAN حول استخراج نموذج جانب الخادم في التعلم الموحد المقسم | سوني منظمة العفو الدولية | AAAI | 2024 | [نشر] [صفحة] [رمز] |
FedDiv: تصفية الضوضاء التعاونية للتعلم الموحد باستخدام الملصقات المزعجة | سيسو؛ جامعة هونغ كونغ | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
محول النقاط مع التعلم الموحد للتنبؤ بحالة سرطان الثدي HER2 من صور الشرائح الكاملة الملطخة بالهيماتوكسيلين والإيوسين | USTC؛ CAS | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
FedNS: خوارزمية رسم سريعة من نوع نيوتن للتعلم الموحد | CAS | AAAI | 2024 | [PUB] [PDF] [الكود] |
قطاع الطرق الموحد X المسلح | جامعة بوردو | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
الأساس الخوارزمي للتعلم الموحد مع البيانات المتسلسلة | GMU | AAAI | 2024 | [حانة] |
UFDA: التكيف العالمي للنطاق الموحد مع افتراضات عملية | XJTU؛ جامعة سيدني | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
FedASMU: التعلم الموحد غير المتزامن الفعال مع تحديث النموذج الديناميكي المدرك للركود | شركة شبكة المعلومات هايثينك رويال فلوش | AAAI | 2024 | [PUB] [صفحة] [PDF] |
المحول الموجه نحو اللغة للتصنيف الموحد متعدد الملصقات | NTU | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
FedCD: التعلم الموحد شبه الخاضع للإشراف مع توازن الوعي الصفي عبر مدرسين مزدوجين | SZU | AAAI | 2024 | [نشر] [صفحة] [رمز] |
ما وراء التهديدات التقليدية: هجوم مستتر مستمر على التعلم الموحد | اليورانيوم عالي التخصيب | AAAI | 2024 | [نشر] [صفحة] [رمز] |
التعلم الموحد مع العملاء المزعجين للغاية عبر التقطير السلبي | XMU | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
FedST: تعلم نقل النمط الموحد لتجزئة الصور غير IID | يو اس تي بي | AAAI | 2024 | [PUB] [صفحة] [学报] [الكود] |
PPIDSG: نظام مشاركة توزيع الصور مع الحفاظ على الخصوصية مع GAN في التعلم الموحد | USTC | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
إطار عمل التوأم الرقمي المعرفي (CDT) القائم على التعلم الموحد (PPFL) للمدن الذكية | وحدة التحكم المركزية | AAAI | 2024 | [حانة] |
خوارزمية أولية مزدوجة للتعلم الموحد المختلط | جامعة نورث وسترن | AAAI | 2024 | [PUB] [صفحة] [PDF] |
FedLF: التعلم الموحد العادل للطبقة الحكيمة | CUHK؛ معهد شنتشن للذكاء الاصطناعي والروبوتات للمجتمع | AAAI | 2024 | [النشرة] [الصفحة] |
نحو التعلم الموحد من خلال الرسم البياني العادل عبر آليات الحوافز | زيجو؛ فو.دو | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
نحو متانة التعلم الموحد الخاص التفاضلي | الخميس | AAAI | 2024 | [النشرة] [الصفحة] |
مقاومة الهجمات الخلفية في التعلم الموحد عبر الانتخابات ثنائية الاتجاه والمنظور الفردي | زيجو؛ هواوي | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
العدد الصحيح يكفي: عندما يلتقي التعلم الموحد العمودي بالتقريب | زيجو؛ مجموعة النمل | AAAI | 2024 | [النشرة] [الصفحة] |
التعلم الموحد الموجه بـ CLIP حول عدم التجانس والبيانات طويلة الذيل | XMU | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
الضبط التكيفي الموحد للتعلم التعاوني متعدد المجالات | فو.دو | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
التعلم الموحد العادل متعدد الأبعاد | SDU | AAAI | 2024 | [PUB] [صفحة] [PDF] |
HiFi-Gas: آلية حوافز التعلم الموحدة الهرمية المحسنة لتقدير استخدام الغاز | مجموعة إن | AAAI | 2024 | [حانة] |
حول دور زخم الخادم في التعلم الموحد | جامعة فرجينيا | AAAI | 2024 | [PUB] [PDF] |
المتنافسون الفيدراليون: تعاون متناغم في التعلم الموحد مع المشاركين المتنافسين | بوبت | AAAI | 2024 | [PUB] [صفحة] [PDF] |
z-SignFedAvg: ضغط عشوائي قائم على الإشارات للتعلم الموحد | CUHK؛ معهد بحوث شنتشن الصيني للبيانات الضخمة | AAAI | 2024 | [PUB] [صفحة] [PDF] |
تباين البيانات وعدم التوفر الزمني - إدراك التعلم الموحد غير المتزامن للصيانة التنبؤية لأساطيل النقل | مجموعة فولكس فاجن | AAAI | 2024 | [النشرة] [الصفحة] |
تعلم الرسم البياني الموحد في ظل تحول المجال باستخدام نماذج أولية قابلة للتعميم | منظمة الصحة العالمية | AAAI | 2024 | [النشرة] [الصفحة] |
TurboSVM-FL: تعزيز التعلم الموحد من خلال تجميع SVM للعملاء الكسالى | الجامعة التقنية في ميونيخ | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
تقليل تناقض التدرج التعاوني متعدد المصادر لتعميم المجال الموحد | TJU | AAAI | 2024 | [PUB] [PDF] [الكود] |
إخفاء العينات الحساسة ضد تسرب التدرج في التعلم الموحد | جامعة موناش | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
FedA3I: التجميع المراعي لجودة التعليقات التوضيحية لتجزئة الصور الطبية الموحدة ضد ضوضاء التعليقات التوضيحية غير المتجانسة | هوست | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
التعلم السببي الموحد مع التحسين التكيفي القابل للتفسير | SDU | AAAI | 2024 | [PUB] [صفحة] [PDF] |
قطاع الطرق المتتالي السياقي الموحد مع الاتصال غير المتزامن والمستخدمين غير المتجانسين | USTC | AAAI | 2024 | [PUB] [صفحة] [PDF] |
استكشاف التعلم الموحد شبه الخاضع للإشراف مرة واحدة باستخدام نماذج النشر المدربة مسبقًا | فو.دو | AAAI | 2024 | [PUB] [PDF] |
التنوع والأصالة أسلوب مقيد بشكل مشترك لتعميم المجال الموحد في إعادة تحديد هوية الشخص | XMU؛ جامعة ترينتو | AAAI | 2024 | [النشرة] [الصفحة] |
PerFedRLNAS: بحث شامل ومخصص في الهندسة العصبية الموحدة | يو تي | AAAI | 2024 | [النشرة] [الصفحة] |
التعلم الموحد غير المتزامن الفعال مع تجميع الزخم المحتمل والتصحيح الدقيق | بوبت | AAAI | 2024 | [النشرة] [الصفحة] |
الهجمات العدائية على خوارزميات معدل البت التكيفي المتعلم الموحد | جامعة هونج كونج | AAAI | 2024 | [حانة] |
FedTGP: نماذج عالمية قابلة للتدريب مع التعلم المقارن المعزز بالهامش التكيفي للبيانات وعدم تجانس النماذج في التعلم الموحد | SJTU | AAAI | 2024 | [PUB] [صفحة] [PDF] [رمز] |
LR-XFL: التعلم الموحد القابل للتفسير القائم على الاستدلال المنطقي | NTU | AAAI | 2024 | [PUB] [PDF] [الكود] |
نهج هوبر لتقليل خسائر التعلم البيزنطي القوي | مختبر تشجيانغ | AAAI | 2024 | [PUB] [صفحة] [PDF] |
تدريب المعلمات المدركة للمعرفة للتعلم الموحد المخصص | جامعة نورث إيسترن | AAAI | 2024 | [النشرة] [الصفحة] |
التعلم الموحد لضوضاء الملصقات مع تنظيم منتجات التنوع المحلي | SJTU | AAAI | 2024 | [PUB] [صفحة] [ملحق] |
التجميع الموزون المُكيَّف في التعلم الموحد (ملخص الطالب) | جامعة كولومبيا البريطانية | AAAI | 2024 | [حانة] |
نقل المعرفة عبر النموذج المدمج في التعلم الموحد (ملخص الطالب) | جامعة سيدني | AAAI | 2024 | [النشرة] [الصفحة] |
PICSR: جهاز توجيه متعدد الصوامع مزود بنموذج أولي للتعلم الموحد (ملخص الطالب) | مختبر أوتون بجامعة ولاية أوهايو، CMU | AAAI | 2024 | [النشرة] [الصفحة] |
شبكة تلافيفية للرسم البياني تحافظ على الخصوصية لتوصية العناصر الموحدة | SZU | منظمة العفو الدولية | 2023 | [حانة] |
الفوز للجميع: إطار عمل موحد يحافظ على الخصوصية للتوصية ذات الهدف المزدوج عبر النطاقات | كاس؛ يوكاس؛ تقنية دينار أردني؛ دينار أردني لأبحاث المدن الذكية | AAAI | 2023 | [حانة] |
هجوم غير مستهدف ضد أنظمة التوصية الموحدة عبر تضمين العناصر السامة والدفاع | USTC؛ مختبر الدولة الرئيسي للذكاء المعرفي | AAAI | 2023 | [PUB] [PDF] [الكود] |
التعهيد الجماعي المعزز بالحوافز | SDU | AAAI | 2023 | [PUB] [PDF] |
معالجة عدم تجانس البيانات في التعلم الموحد باستخدام النماذج الأولية للفصل | جامعة ليهاي | AAAI | 2023 | [PUB] [PDF] [الكود] |
FairFed: تمكين عدالة المجموعة في التعلم الموحد | جامعة جنوب كاليفورنيا | AAAI | 2023 | [PUB] [PDF] [ترجمة] |
نشر المتانة الفيدرالية: مشاركة المتانة التنافسية في التعلم الفيدرالي غير المتجانس | جامعة ولاية ميشيغان | AAAI | 2023 | [حانة] |
التشتت المكمل: تشذيب نموذج منخفض التكلفة للتعلم الموحد | إنجيت | AAAI | 2023 | [حانة] |
اتصالات مجانية تقريبًا في تحديد أفضل ذراع اتحادي | جامعة سنغافورة الوطنية | AAAI | 2023 | [PUB] [PDF] |
تجميع النماذج التكيفية على مستوى الطبقة من أجل التعلم الموحد القابل للتطوير | جامعة جنوب كاليفورنيا، جامعة إنها | AAAI | 2023 | [PUB] [PDF] |
التسمم بـ Cerberus: هجوم خلفي خفي ومتواطئ ضد التعلم الفيدرالي | بجتو | AAAI | 2023 | [حانة] |
FedMDFG: التعلم الموحد مع النسب متعدد التدرج والتوجيه العادل | CUHK؛ معهد شنتشن للذكاء الاصطناعي والروبوتات من أجل المجتمع | AAAI | 2023 | [حانة] |
تأمين التجميع الآمن: التخفيف من تسرب الخصوصية متعدد الجولات في التعلم الموحد | جامعة جنوب كاليفورنيا | AAAI | 2023 | [PUB] [PDF] [فيديو] [رمز] |
التعلم الموحد على الرسوم البيانية غير IID عبر تبادل المعرفة الهيكلية | UTS | AAAI | 2023 | [PUB] [PDF] [الكود] |
تحديد تشابه التوزيع الفعال في التعلم الموحد العنقودي عبر الزوايا الرئيسية بين المساحات الفرعية لبيانات العميل | جامعة كاليفورنيا سان دييغو | AAAI | 2023 | [PUB] [PDF] [الكود] |
FedABC: استهداف المنافسة العادلة في التعلم الفيدرالي المخصص | منظمة الصحة العالمية؛ مختبر هوبى لوجيا؛ أكاديمية جي دي اكسبلور | AAAI | 2023 | [PUB] [PDF] |
ما بعد ADMM: إطار التعلم الموحد المتكيف مع تقليل تباين العميل | سوتد | AAAI | 2023 | [PUB] [PDF] |
FedGS: أخذ العينات المستندة إلى الرسم البياني الموحد مع توفر العميل التعسفي | XMU | AAAI | 2023 | [PUB] [PDF] [الكود] |
التعلم الموحد الأسرع والتكيف | جامعة بيتسبرغ | AAAI | 2023 | [PUB] [PDF] |
FedNP: نحو التعلم الموحد غير IID عبر الانتشار العصبي الموحد | HKUST | AAAI | 2023 | [PUB] [الكود] [فيديو] [ملحق] |
المطابقة العصبية الموحدة البايزية التي تكمل المعلومات الكاملة | TJU | AAAI | 2023 | [PUB] [PDF] |
CDMA: خوارزمية تعليمية موحدة عملية عبر الأجهزة لحل مشكلات Minimax العامة | ZJU | AAAI | 2023 | [PUB] [PDF] [الكود] |
النموذج التوليدي الموحد للبيانات غير المتجانسة متعددة المصادر في إنترنت الأشياء | جي إس يو | AAAI | 2023 | [حانة] |
DeFL: الدفاع ضد هجمات التسمم النموذجية في التعلم الموحد من خلال التوعية بفترات التعلم الحرجة | جامعة ولاية نيويورك-بينجهامتون | AAAI | 2023 | [حانة] |
FedALA: التجميع المحلي التكيفي للتعلم الموحد المخصص | SJTU | AAAI | 2023 | [PUB] [PDF] [الكود] |
الخوض في المتانة التنافسية للتعلم الموحد | ZJU | AAAI | 2023 | [PUB] [PDF] |
حول ضعف الدفاعات الخلفية للتعلم الموحد | TJU | AAAI | 2023 | [PUB] [PDF] [الكود] |
صدى الجيران: تضخيم الخصوصية للتعلم الموحد الخاص المخصص باستخدام نموذج خلط ورق اللعب | شرطة ألستر الملكية؛ مركز البحوث الهندسية التابع لوزارة التربية والتعليم حول قاعدة البيانات وذكاء الأعمال | AAAI | 2023 | [PUB] [PDF] |
DPAUC: حساب AUC الخاص التفاضلي في التعلم الموحد | شركة بايت دانس | مسارات AAAI الخاصة | 2023 | [PUB] [PDF] [الكود] |
التدريب الفعال لنماذج تشخيص الأعطال الصناعية واسعة النطاق من خلال التسرب الانتهازي الموحد | NTU | برامج AAAI الخاصة | 2023 | [PUB] [PDF] |
التعلم الموحد المنسق على مستوى الصناعة لاكتشاف الأدوية | جامعة كو لوفين | برامج AAAI الخاصة | 2023 | [PUB] [PDF] [فيديو] |
أداة مراقبة التعلم الموحدة لمحاكاة السيارات ذاتية القيادة (ملخص الطالب) | CNU | برامج AAAI الخاصة | 2023 | [حانة] |
MGIA: هجوم الانعكاس المتدرج المتبادل في التعلم الموحد متعدد الوسائط (ملخص الطالب) | بولي يو | برامج AAAI الخاصة | 2023 | [حانة] |
التعلم الموحد المجمع للبيانات غير المتجانسة (ملخص الطالب) | RUC | برامج AAAI الخاصة | 2023 | [حانة] |
FedSampling: استراتيجية أفضل لأخذ العينات للتعلم الموحد | الخميس | IJCAI | 2023 | [PUB] [PDF] [الكود] |
HyperFed: استكشاف النماذج الأولية الزائدية مع التجميع المتسق للبيانات غير IID في التعلم الموحد | ZJU | IJCAI | 2023 | [PUB] [PDF] |
FedOBD: التسرب الانتهازي لتدريب الشبكات العصبية واسعة النطاق بكفاءة من خلال التعلم الموحد | NTU | IJCAI | 2023 | [PUB] [PDF] [الكود] |
نمذجة توزيع التفضيلات الاحتمالية الموحدة مع التجميع المشترك للاكتناز من أجل توصيات متعددة المجالات للحفاظ على الخصوصية | ZJU | IJCAI | 2023 | [حانة] |
الرسم البياني الموحد التعلم الدلالي والهيكلي | منظمة الصحة العالمية | IJCAI | 2023 | [حانة] |
BARA: آلية حوافز فعالة مع تخصيص ميزانية المكافآت عبر الإنترنت في التعلم الموحد عبر الصوامع | سيسو | IJCAI | 2023 | [PUB] [PDF] |
FedDWA: التعلم الموحد المخصص مع تعديل الوزن الديناميكي | سيسو | IJCAI | 2023 | [PUB] [PDF] |
FedPass: التعلم العميق الموحد العمودي الذي يحافظ على الخصوصية مع التشويش التكيفي | البنك الالكتروني | IJCAI | 2023 | [PUB] [PDF] |
وحدة التشفير التلقائي للرسم البياني المتسق عالميًا للرسومات البيانية غير IID | FZU | IJCAI | 2023 | [حانة] [الكود] |
التعلم المعزز التنافسي التعاوني متعدد الوكلاء من أجل التعلم الموحد القائم على المزاد | NTU | IJCAI | 2023 | [حانة] |
التخصيص المزدوج بناء على التوصية الموحدة | JLU؛ جامعة التكنولوجيا سيدني | IJCAI | 2023 | [PUB] [PDF] [الكود] |
FedNoRo: نحو التعلم الموحد القوي من خلال معالجة عدم التوازن الطبقي وعدم تجانس الضوضاء في العلامات | هوست | IJCAI | 2023 | [PUB] [PDF] [الكود] |
رفض الخدمة أو التحكم الدقيق: نحو نماذج مرنة لهجمات التسمم على التعلم الموحد | جامعة شيانغتان | IJCAI | 2023 | [PUB] [PDF] [الكود] |
FedHGN: إطار عمل موحد للشبكات العصبية ذات الرسم البياني غير المتجانس | CUHK | IJCAI | 2023 | [PUB] [PDF] [الكود] |
FedET: إطار التعلم التزايدي الموحد الذي يتسم بكفاءة الاتصال ويعتمد على المحول المحسن | تكنولوجيا بينغ آن؛ الخميس | IJCAI | 2023 | [PUB] [PDF] |
التعلم الموحد الفوري للتنبؤ بالطقس: نحو نماذج أساسية لبيانات الأرصاد الجوية | UTS | IJCAI | 2023 | [PUB] [PDF] [الكود] |
FedBFPT: إطار تعليمي موحد فعال لمزيد من التدريب المسبق لبيرت | ZJU | IJCAI | 2023 | [حانة] [الكود] |
التعلم الموحد بايزي: دراسة استقصائية | مسار مسح IJCAI | 2023 | [بي دي إف] | |
مسح للتقييم الموحد في التعلم الموحد | جامعة ماكواري | مسار مسح IJCAI | 2023 | [PUB] [PDF] |
سامبا: إطار عام لتأمين قطاع الطرق الفيدراليين متعددي الأذرع (ملخص موسع) | مركز INSA فال دي لوار | مسار مجلة IJCAI | 2023 | [حانة] |
تكلفة الاتصالات للأمان والخصوصية في تقدير التردد الموحد | ستانفورد | أستاتس | 2023 | [حانة] [الكود] |
التعلم الموحد الفعال وخفيف الوزن عبر التسرب الموزع غير المتزامن | جامعة رايس | أستاتس | 2023 | [حانة] [الكود] |
التعلم الموحد في ظل انجراف المفهوم الموزع | جامعة كارنيجي ميلون | أستاتس | 2023 | [حانة] [الكود] |
توصيف هجمات التهرب الداخلي في التعلم الموحد | جامعة كارنيجي ميلون | أستاتس | 2023 | [حانة] [الكود] |
المقاربات الموحدة: نموذج لمقارنة خوارزميات التعلم الموحدة | ستانفورد | أستاتس | 2023 | [حانة] [الكود] |
التعلم الموحد الخاص غير المحدب بدون خادم موثوق به | جامعة جنوب كاليفورنيا | أستاتس | 2023 | [حانة] [الكود] |
التعلم الموحد لتدفقات البيانات | جامعة ́ e Cˆ ote d'Azur | أستاتس | 2023 | [حانة] [الكود] |
لا شيء سوى الندم - الاكتشاف السببي الموحد للحفاظ على الخصوصية | مركز هيلمهولتز لأمن المعلومات | أستاتس | 2023 | [حانة] [الكود] |
هجوم استدلال العضوية النشطة في ظل الخصوصية التفاضلية المحلية في التعلم الموحد | UFL | أستاتس | 2023 | [حانة] [الكود] |
ديناميكيات لانجفين المتوسطة الموحدة: نحو نظرية موحدة وخوارزميات جديدة | CMAP | أستاتس | 2023 | [حانة] |
التعلم البيزنطي القوي الموحد بمعدلات إحصائية مثالية | جامعة كاليفورنيا في بيركلي | أستاتس | 2023 | [حانة] [الكود] |
التعلم الموحد على الرسوم البيانية غير IID عبر تبادل المعرفة الهيكلية | UTS | AAAI | 2023 | [PDF] [الكود] |
FedGS: أخذ العينات المستندة إلى الرسم البياني الموحد مع توفر العميل التعسفي | XMU | AAAI | 2023 | [PDF] [الكود] |
التعهيد الجماعي المعزز بالحوافز | SDU | AAAI | 2023 | [بي دي إف] |
نحو فهم اختيار العميل المتحيز في التعلم الموحد. | جامعة كارنيجي ميلون | أستاتس | 2022 | [حانة] [الكود] |
FLIX: بديل بسيط وفعال في مجال الاتصالات للأساليب المحلية في التعلم الموحد | كاوست | أستاتس | 2022 | [PUB] [PDF] [الكود] |
الحدود الحادة للمتوسط الموحد (SGD المحلي) والمنظور المستمر. | ستانفورد | أستاتس | 2022 | [PUB] [PDF] [الكود] |
التعلم المعزز الموحد مع عدم تجانس البيئة. | بيلة الفينيل كيتون | أستاتس | 2022 | [PUB] [PDF] [الكود] |
الكشف الموحد عن مجتمع قصر النظر من خلال الاتصال مرة واحدة | بوردو | أستاتس | 2022 | [PUB] [PDF] |
خوارزميات ربط الثقة العليا غير المتزامنة لقطاع الطرق الخطي الموحد. | جامعة فرجينيا | أستاتس | 2022 | [PUB] [PDF] [الكود] |
نحو تعلم بنية شبكة بايزي الموحدة مع التحسين المستمر. | جامعة كارنيجي ميلون | أستاتس | 2022 | [PUB] [PDF] [الكود] |
التعلم الموحد مع التجميع غير المتزامن المخزن | ميتا الذكاء الاصطناعي | أستاتس | 2022 | [PUB] [PDF] [فيديو] |
التعلم الموحد الخاص التفاضلي على البيانات غير المتجانسة. | ستانفورد | أستاتس | 2022 | [PUB] [PDF] [الكود] |
SparseFed: التخفيف من هجمات التسمم النموذجية في التعلم الموحد باستخدام التشتت | برينستون | أستاتس | 2022 | [PUB] [PDF] [الكود] [فيديو] |
مسائل الأساس: أفضل أساليب من الدرجة الثانية فعالة للاتصالات للتعلم الفدرالي | Kaust | Aistats | 2022 | [حانة] [PDF] |
تعزيز التدرج الوظيفي الفيدرالي. | جامعة بنسلفانيا | Aistats | 2022 | [PUB] [PDF] [رمز] |
QLSD: ديناميات لانجفين العشوائية الكمية للتعلم الفيدرالي بايزي. | مختبر Criteo AI | Aistats | 2022 | [PUB] [PDF] [رمز] [فيديو] |
استقراء المعرفة المستندة إلى التعلم التلوي للرسوم البيانية المعرفة في الإعداد الموحدة kg. | زجو | ijcai | 2022 | [PUB] [PDF] [رمز] |
التعلم الفدرالي المخصص مع رسم بياني | UTS | ijcai | 2022 | [PUB] [PDF] [رمز] |
الشبكة العصبية الرسم البياني الموحدة رأسياً لتصنيف عقدة الحفاظ على الخصوصية | زجو | ijcai | 2022 | [حانة] [PDF] |
التكيف مع التكيف: التعلم التخصيص للتعلم الفدرالي المتقاطع | ijcai | 2022 | [PUB] [PDF] [رمز] | |
نقل المعرفة فرقة غير متجانسة لتدريب نماذج كبيرة في التعلم الفدرالي | ijcai | 2022 | [حانة] [PDF] | |
التعلم الاتحادي شبه الخاضع للإشراف. | ijcai | 2022 | [حانة] | |
التعلم الاتحادي المستمر بناءً على تقطير المعرفة. | ijcai | 2022 | [حانة] | |
التعلم الاتحادي على بيانات غير متجانسة وطويلة الذيل عبر المصنف إعادة التدريب مع الميزات الفيدرالية | ijcai | 2022 | [PUB] [PDF] [رمز] | |
انتباه متعدد المهام للاعتراف بنشاط الإنسان عبر الأفراد | ijcai | 2022 | [حانة] | |
التعلم الفدرالي الشخصية مع التعميم السياق. | ijcai | 2022 | [حانة] [PDF] | |
التدريع التعلم الفيدرالي: تجميع قوي مع اختيار العميل التكيفي. | ijcai | 2022 | [حانة] [PDF] | |
FEDCG: الاستفادة من GAN المشروطة لحماية الخصوصية والحفاظ على الأداء التنافسي في التعلم الفيدرالي | ijcai | 2022 | [PUB] [PDF] [رمز] | |
FEDDUAP: التعلم الاتحادي مع التحديث الديناميكي والتقليم التكيفي باستخدام البيانات المشتركة على الخادم. | ijcai | 2022 | [حانة] [PDF] | |
نحو التعلم الفيدرالي الذي يمكن التحقق منه surv. | ijcai | 2022 | [حانة] [PDF] | |
Harmofl: تنسيق الانجرافات المحلية والعالمية في التعلم الفدرالي على الصور الطبية غير المتجانسة | cuhk بوا | aaai | 2022 | [PUB] [PDF] [رمز] [解读] |
التعلم الاتحادي للاعتراف بالوجه مع تصحيح التدرج | bupt | aaai | 2022 | [حانة] [PDF] |
Spreadgnn: لا مركزية متعددة المهام التعلم الفدرالي للشبكات العصبية الرسم البياني على البيانات الجزيئية | جامعة جنوب كاليفورنيا | aaai | 2022 | [PUB] [PDF] [رمز] [解读] |
SmartIdx: تقليل تكلفة الاتصال في التعلم الفدرالي عن طريق استغلال هياكل CNNS | يضرب؛ PCL | aaai | 2022 | [حانة] [رمز] |
سد بين إشارات المعالجة المعرفية والميزات اللغوية عبر شبكة انتباه موحدة | tju | aaai | 2022 | [حانة] [PDF] |
الاستيلاء على فترات التعلم النقدية في التعلم الفيدرالي | جامعة ولاية نيويورك بينغهامتون | aaai | 2022 | [حانة] [PDF] |
تنسيق MOMENTA للتعلم الفدرالي المتقاطع | جامعة بيتسبرغ | aaai | 2022 | [حانة] [PDF] |
FedProto: تعلم النموذج الأولي المودري على الأجهزة غير المتجانسة | UTS | aaai | 2022 | [PUB] [PDF] [رمز] |
FedSoft: التعلم الاتحادي المجمع الناعم مع التحديث المحلي القريب | CMU | aaai | 2022 | [PUB] [PDF] [رمز] |
التدريب الديناميكي المتناثر الموحدة: الحوسبة أقل ، والتواصل أقل ، ولكن التعلم بشكل أفضل | جامعة تكساس في أوستن | aaai | 2022 | [PUB] [PDF] [رمز] |
FedFR: إطار عمل فدرالي التحسين المشترك للتعرف على الوجه العام والشخصي | جامعة تايوان الوطنية | aaai | 2022 | [PUB] [PDF] [رمز] |
Splitfed: عندما يلتقي التعلم الاتحادي | CSIRO | aaai | 2022 | [PUB] [PDF] [رمز] |
جدولة الجهاز الفعال مع التعلم الفيدرالي متعدد الأسعار | جامعة سوتشو | aaai | 2022 | [حانة] [PDF] |
محاذاة تدرج ضمني في التعلم الموزع والاتحادي | IIT Kanpur | aaai | 2022 | [حانة] [PDF] |
أقرب تصنيف جار مع مستعمرة من الفاكهة | IBM Research | aaai | 2022 | [PUB] [PDF] [رمز] |
حقول المتجهات المتكررة والمحافظة ، مع تطبيقات التعلم الموحدة. | جوجل | بديل | 2022 | [حانة] [PDF] |
التعلم المودري مع الخصوصية المتضخمة | ijcai | 2021 | [حانة] [PDF] [فيديو] | |
يقلد السلوك توزيع: الجمع بين السلوكيات الفردية والجماعية للتعلم الموحدة | ijcai | 2021 | [حانة] [PDF] | |
FedSpeech: نص إلى خطاب الاتحاد مع التعلم المستمر | ijcai | 2021 | [حانة] [PDF] | |
التعلم العملي للصفائح الواحدة لإنشاء السيلو المتقاطع | ijcai | 2021 | [PUB] [PDF] [رمز] | |
تقطير النموذج المودري مع خصوصية تفاضلية خالية من الضوضاء | ijcai | 2021 | [حانة] [PDF] [فيديو] | |
LDP-FL: التجميع الخاص العملي في التعلم الفدرالي مع الخصوصية التفاضلية المحلية | ijcai | 2021 | [حانة] [PDF] | |
التعلم الاتحادي مع متوسط العادل. | ijcai | 2021 | [PUB] [PDF] [رمز] | |
H-FL: بنية هرمية فعالة للاتصالات والخصوصية للتعلم الفيدرالي. | ijcai | 2021 | [حانة] [PDF] | |
التواصل الفعال والقابل للتطوير لا مركزية التعلم الحافة الموحدة. | ijcai | 2021 | [حانة] | |
تأمين التعلم العمودي غير المتزامن غير المتزامن مع التحديث للخلف | جامعة شيديان JD Tech | aaai | 2021 | [حانة] [PDF] [فيديو] |
FedRec ++: توصية فدرالية خاسرة مع ردود فعل صريحة | Szu | aaai | 2021 | [حانة] [فيديو] |
قطاع الطرق متعدد السلاح | جامعة فرجينيا | aaai | 2021 | [PUB] [PDF] [رمز] [فيديو] |
على تقارب SGD المحلي الموفرة للاتصالات للتعلم الفدرالي | جامعة تيمبل جامعة بيتسبرغ | aaai | 2021 | [حانة] [فيديو] |
اللهب: التعلم الاتحادي الخاص تفاضليًا في نموذج خلط ورق اللعب | جامعة رينمين في الصين ؛ جامعة كيوتو | aaai | 2021 | [PUB] [PDF] [فيديو] [رمز] |
نحو فهم تأثير العملاء الأفراد في التعلم الفيدرالي | Sjtu ؛ جامعة تكساس في دالاس | aaai | 2021 | [حانة] [PDF] [فيديو] |
التعلم الفيدرالي الآمن على نحو آمن ضد العملاء الخبيثين | جامعة ديوك | aaai | 2021 | [حانة] [PDF] [فيديو] [شريحة] |
التعلم الفدرالي المتقاطع المتقاطع على البيانات غير IID | جامعة سيمون فريزر ؛ جامعة ماكماستر | aaai | 2021 | [PUB] [PDF] [فيديو] [UC.] |
ألعاب مشاركة النماذج: تحليل التعلم الفيدرالي تحت المشاركة الطوعية | جامعة كورنيل | aaai | 2021 | [PUB] [PDF] [رمز] [فيديو] |
لعنة أو الخلاص؟ كيف يؤثر عدم تجانس البيانات على متانة التعلم الفيدرالي | جامعة نيفادا IBM Research | aaai | 2021 | [حانة] [PDF] [فيديو] |
لعبة التدرجات: تخفيف عملاء غير ذي صلة في التعلم الفيدرالي | IIT بومباي IBM Research | aaai | 2021 | [PUB] [PDF] [رمز] [فيديو] [supp] |
مخطط نزول تنسيق الكتل الفدرالية لتعلم النماذج العالمية والشخصية | cuhk جامعة ولاية أريزونا | aaai | 2021 | [PUB] [PDF] [فيديو] [رمز] |
معالجة اختلال التوازن في التعلم الفدرالي | جامعة نورث وسترن | aaai | 2021 | [PUB] [PDF] [فيديو] [رمز] [解读] |
الدفاع ضد الخلفيات في التعلم الفيدرالي بمعدل تعليمي قوي | جامعة تكساس في دالاس | aaai | 2021 | [PUB] [PDF] [فيديو] [رمز] |
هجمات المتسابق الحر على تجميع النماذج في التعلم الموحدة | مختبرات Accenture | Aistats | 2021 | [PUB] [PDF] [رمز] [فيديو] [supp] |
خصوصية F-Differential Federated | جامعة بنسلفانيا | Aistats | 2021 | [PUB] [رمز] [فيديو] [SUPR] |
التعلم المودري مع الضغط: تحليل موحد وضمانات حادة | جامعة ولاية بنسلفانيا ؛ جامعة تكساس في أوستن | Aistats | 2021 | [PUB] [PDF] [رمز] [فيديو] [supp] |
نموذج خلط للخصوصية التفاضلية في التعلم الفيدرالي | UCLA جوجل | Aistats | 2021 | [حانة] [فيديو] [Supp] |
مقايضات التقارب والدقة في التعلم الفدرالي والتعلم التلوي | جوجل | Aistats | 2021 | [PUB] [PDF] [فيديو] [SUPR] |
قطاع الطرق متعدد السلاح مع التخصيص | جامعة فرجينيا جامعة ولاية بنسلفانيا | Aistats | 2021 | [PUB] [PDF] [رمز] [فيديو] [supp] |
نحو مشاركة الجهاز المرنة في التعلم الفيدرالي | CMU Sysu | Aistats | 2021 | [PUB] [PDF] [فيديو] [SUPR] |
التعلم الفوقي المتدرب للكشف عن بطاقات الائتمان الاحتيالية | ijcai | 2020 | [حانة] [فيديو] | |
لعبة متعددة اللاعبين لدراسة مخططات الحوافز التعليمية الفدرالية | ijcai | 2020 | [حانة] [رمز] [解读] | |
التدرج العملي العملي يعزز أشجار القرار | NUS UWA | aaai | 2020 | [PUB] [PDF] [رمز] |
التعلم المودري لمشاكل التأريض في الرؤية واللغة | PKU تينسنت | aaai | 2020 | [حانة] |
تخصيص Dirichlet الكامنة الموحدة: إطار عمل محلي على أساس الخصوصية التفاضلية | بوا | aaai | 2020 | [حانة] |
تجزئة المريض الفيدرالي | جامعة كورنيل | aaai | 2020 | [حانة] |
التعلم الفدرالي القوي عبر تدريس الآلات التعاونية | مختبرات الأبحاث Symantec ؛ Kaust | aaai | 2020 | [حانة] [PDF] |
FedVision: منصة الكشف عن الكائنات المرئية عبر الإنترنت مدعوم من التعلم الفدرالي | Webank | aaai | 2020 | [PUB] [PDF] [رمز] |
FedPaq: طريقة تعليمية فدرالية فعالة للاتصالات مع متوسط ومتوسط دوري | UC سانتا باربرا. يوت أوستن | Aistats | 2020 | [PUB] [PDF] [فيديو] [SUPR] |
كيفية التعلم الموحد في الباب الخلفي | كورنيل تك | Aistats | 2020 | [Pub] [PDF] [فيديو] [رمز] [Supp] |
اكتشاف الضاربون الثقيلون المتدربون بخصوصية تفاضلية | RPI ؛ جوجل | Aistats | 2020 | [PUB] [PDF] [فيديو] [SUPR] |
تصور متعدد الوكلاء لشرح التعلم الفيدرالي | Webank | ijcai | 2019 | [حانة] [فيديو] |
أوراق التعلم الموحدة المقبولة في مؤتمر ومجلة أفضل ML (التعلم الآلي) ، بما في ذلك Neurips (المؤتمر السنوي لأنظمة معالجة المعلومات العصبية) ، ICML (المؤتمر الدولي للتعلم الآلي) ، ICLR (المؤتمر الدولي لتمثيلات التعلم) ، كولت (الحساب السنوي للمؤتمرات السنوية نظرية التعلم) ، UAI (مؤتمر حول عدم اليقين في الذكاء الاصطناعي) ، التعلم الآلي ، JMLR (مجلة أبحاث التعلم الآلي) ، TPAMI (IEEE المعاملات على تحليل النمط وذكاء الآلة).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
استقرار وتسريع التعلم الفيدرالي على البيانات غير المتجانسة بمشاركة عميل جزئي | tpami | 2025 | [حانة] | |
نموذج الاتحادي الطبي مع مزيج من المكونات الشخصية والمشتركة | tpami | 2025 | [حانة] | |
التعلم الاتحادي لقطة واحدة عن طريق اتصال تقطير التقطير الاصطناعية | العصبية | 2024 | [حانة] | |
التعلم المودري غير المدمر على مواد فرعية سلسة مضغوطة مع بيانات غير متجانسة | العصبية | 2024 | [حانة] | |
FedGMKD: إطار تعليمي أولي فعال من خلال تقطير المعرفة وتجميع يدرك التناقض | العصبية | 2024 | [حانة] | |
تحسين التعميم في التعلم الفدرالي مع تنظيم المعلومات المتبادلة النموذجية: نهج الاستدلال الخلفي | العصبية | 2024 | [حانة] | |
نموذج مودري غير متجانس تمثيل Matryoshka التعلم | العصبية | 2024 | [حانة] | |
تعلم الرسم البياني المودري للتوصية عبر المجال | العصبية | 2024 | [حانة] | |
FedGmark: العلامة المائية القوية على شهادة لتعلم الرسم البياني الفدرالي | العصبية | 2024 | [حانة] | |
محول مزدوج للشخصية لنماذج الأساس الفيدرالية | العصبية | 2024 | [حانة] | |
أساليب التدرج الطبيعي للسياسة الطبيعية والأساليب الناقدة للممثل للتعلم التعزيز متعدد المهام | العصبية | 2024 | [حانة] | |
ترويض الذيل الطويل في التنبؤ بالتنقل البشري | العصبية | 2024 | [حانة] | |
الدفاع المزدوج: تعزيز الخصوصية وتخفيف هجمات التسمم في التعلم الفدرالي | العصبية | 2024 | [حانة] | |
مُحسّنات محسنة للرسوم البيانية لتوصية التوصية التي تدرك الهيكل | العصبية | 2024 | [حانة] | |
DOFIT: توليف تعليمات الاتحادية مدروسة مع النسيان الكارثي المخفف | العصبية | 2024 | [حانة] | |
التعلم الاتحادي الفعال ضد عدم توفر العميل غير المتجانسة وغير الثابتة | العصبية | 2024 | [حانة] | |
المحول الفيدرالي: التعلم الموحدين العمودي متعدد الأطراف على البيانات العملية المرتبطة بشكل غامض | العصبية | 2024 | [حانة] | |
FIARSE: التعلم الفدرالي المتداخل النموذجي من خلال استخراج عارضة الأزياء الأهمية | العصبية | 2024 | [حانة] | |
التثبيت الفوري الاحتمالي الموحدة مع بيانات غير متوازنة وغير متوازنة | العصبية | 2024 | [حانة] | |
فلورا: نماذج لغة كبيرة صياغة مع تكيفات غير متجانسة منخفضة الرتبة | العصبية | 2024 | [حانة] | |
ترويض تباين تمثيل المجال المتقاطع في تعلم النموذج الأولي الموحد مع مجالات البيانات غير المتجانسة | العصبية | 2024 | [حانة] | |
PFEDClub: تجميع النموذج غير المتجانس يمكن التحكم فيه للتعلم الفدرالي الشخصي | العصبية | 2024 | [حانة] | |
لماذا تام؟ رفع التعلم الفدرالي من خلال تحديثات الشبكة الجزئية | العصبية | 2024 | [حانة] | |
FUSEFL: التعلم الاتحادي لصفائح واحدة من خلال عدسة السببية مع اندماج النموذج التدريجي | العصبية | 2024 | [حانة] | |
FedSSP: تعلم الرسم البياني المودري مع المعرفة الطيفية والتفضيل الشخصي | العصبية | 2024 | [حانة] | |
معالجة التعلم من مساحات الميزات غير المتجانسة مع استغلال الملصقات الصريح | العصبية | 2024 | [حانة] | |
A-FEDPD: محاذاة الانجراف المزدوج هو جميع احتياجات التعلم الدبالية الأولية | العصبية | 2024 | [حانة] | |
تقدير التردد الخاص والشخصي في بيئة اتحادية | العصبية | 2024 | [حانة] | |
مفاضلة تعقيد التعقيد في العينة في التعلم Q Federated | العصبية | 2024 | [حانة] | |
التعرف على التعزيز غير المتصلة بالإنترنت الموحدة | العصبية | 2024 | [حانة] | |
التكيف السوداء الموحدة للتجزئة الدلالية | العصبية | 2024 | [حانة] | |
التفكير إلى الأمام: فني في إطار عمل نماذج اللغة الموفرة للذاكرة | العصبية | 2024 | [حانة] | |
التعلم الفدرالي من نماذج مؤسسة Language Models: التحليل والطريقة النظرية | العصبية | 2024 | [حانة] | |
التصميم الأمثل لاستنباط التفضيل البشري | العصبية | 2024 | [حانة] | |
تجاه الأجهزة المتنوعة غير المتجانسة التعلم المودري عبر تكامل المعرفة الحسابية المهمة | العصبية | 2024 | [حانة] | |
التعلم الفدرالي الشخصي عبر تكيف توزيع الميزات | العصبية | 2024 | [حانة] | |
SAFFLSA: ترويض عدم التجانس في التقريب العشوائي الخطي الموحدة والتعلم TD | العصبية | 2024 | [حانة] | |
نهج بايز للتعلم الفدرالي المخصص في أماكن غير متجانسة | العصبية | 2024 | [حانة] | |
RFLPA: إطار عمل تعليمي قوي ضد التسمم بتجميع آمن | العصبية | 2024 | [حانة] | |
FedGTST: تعزيز قابلية نقل النماذج الفيدرالية عن طريق ضبط الإحصاءات | العصبية | 2024 | [حانة] | |
التجميع القابل للتعلم من طرف إلى طرف للتعلم القصد في التوصية | العصبية | 2024 | [حانة] | |
FedLPA: التعلم الاتحادي لقطة واحدة مع تجميع خلفي من الطبقات | العصبية | 2024 | [حانة] | |
Time-FFM: نحو نموذج مؤسسة Federated Feedered لتنبؤ السلاسل الزمنية | العصبية | 2024 | [حانة] | |
FOOGD: التعاون المودري لكل من التعميم والاكتشاف خارج التوزيع | العصبية | 2024 | [حانة] | |
سكين الجيش السويسري للتعلم الفيدرالي غير المتجانس: اقتران مرن عبر Trace Norm | العصبية | 2024 | [حانة] | |
Fedne: الجار الفيدرالي بمساعدة بديل | العصبية | 2024 | [حانة] | |
التدريب المحلي المنخفض الدقة يكفي للتعلم الفيدرالي | العصبية | 2024 | [حانة] | |
مدرك للموارد التعلم الخاضع للإشراف ذاتيا مع تمثيلات الطبقة العالمية | العصبية | 2024 | [حانة] | |
حول ضرورة التعاون لاختيار النماذج عبر الإنترنت مع البيانات اللامركزية | العصبية | 2024 | [حانة] | |
قوة الاستقراء في التعلم الفيدرالي | العصبية | 2024 | [حانة] | |
(fl) $^2 $: التغلب على بعض الملصقات في التعلم شبه الخاضع للإشراف الفدرالي | العصبية | 2024 | [حانة] | |
على استراتيجيات أخذ العينات لارتفاع طيور النموذج | العصبية | 2024 | [حانة] | |
تخصيص نماذج اللغة مع LORA الحكيمة للحصول على توصية متسلسلة | العصبية | 2024 | [حانة] | |
SPAFL: التعلم الاتحادي فعال الاتصال مع النماذج المتفرقة والنفقات الحاسوبية المنخفضة | العصبية | 2024 | [حانة] | |
Hydra-FL: تقطير المعرفة المختلط للتعلم الفيدرالي القوي والدقيق | العصبية | 2024 | [حانة] | |
أساليب نقطة القريبة المستقرة للتحسين الفدرالي | العصبية | 2024 | [حانة] | |
Dapperfl: التعلم الفدرالي التكيفي المجال مع اندماج النموذج لأجهزة الحافة | العصبية | 2024 | [حانة] | |
تشريح تباينات المعلمة للدفاع عن الورق الخلفي في التعلم المودري غير المتجانس | العصبية | 2024 | [حانة] | |
هل أسوأ وكيل أداء يقود الحزمة؟ تحليل ديناميات الوكيل في SGD الموزع الموحد | العصبية | 2024 | [حانة] | |
FedavP: زيادة البيانات المحلية عبر السياسة المشتركة في التعلم الموحدة | العصبية | 2024 | [حانة] | |
كوبو: التعلم التعاوني عبر تحسين الفلز | العصبية | 2024 | [حانة] | |
تحليل التقارب من التعلم الفدرالي المنقسمة على البيانات غير المتجانسة | العصبية | 2024 | [حانة] | |
مجموعة الاتحادية الموفرة للاتصالات توزيعيًا قويًا | العصبية | 2024 | [حانة] | |
فيراري: ميزة الاتحادية غير المعروفة من خلال تحسين حساسية الميزة | العصبية | 2024 | [حانة] | |
التعلم الاتحادي على أوضاع متصلة | العصبية | 2024 | [حانة] | |
التعلم الاتحادي المخصص مع مزيج من النماذج للتنبؤ التكيفي والضغط الدقيق النموذجي | العصبية | 2024 | [حانة] | |
هل يؤدي الإنصاف المساواة إلى عدم الاستقرار؟ حدود الإنصاف في التعلم الفدرالي المستقر تحت السلوكيات الإيثار | العصبية | 2024 | [حانة] | |
تنبؤ عبر الإنترنت من الخبراء الذين يعانون من الخصوصية التفاضلية: الانفصال والأسف السريع | العصبية | 2024 | [حانة] | |
DataStealing: سرقة البيانات من نماذج الانتشار في التعلم الفدرالي مع أحصنة طروادة متعددة | العصبية | 2024 | [حانة] | |
الطائرات السلوكية الموحدة: شرح تطور سلوك العميل في التعلم الفدرالي | العصبية | 2024 | [حانة] | |
التعلم الفدرالي الهرمي مع تصحيح التدرج متعدد الجوانب | العصبية | 2024 | [حانة] | |
Hyperprism: إطار تجميع غير خطي تكيفي للتعلم الآلي الموزع على البيانات غير IID وروابط الاتصالات المتغيرة بالوقت | العصبية | 2024 | [حانة] | |
الرمح: انعكاس متدرج دقيق من الدُفعات في التعلم الفدرالي | العصبية | 2024 | [حانة] | |
التعلم المودري تحت مشاركة العميل الدورية والبيانات غير المتجانسة: خوارزمية جديدة كفاءة الاتصالات | العصبية | 2024 | [حانة] | |
فجوات الجسور: تجميعات متعددة المراكز الموحدة في وجهات نظر هجينة غير متجانسة | العصبية | 2024 | [حانة] | |
التعلم الاتحادي المقاوم للارتباك عبر تنسيق البيانات القائم على الانتشار على البيانات غير IID | العصبية | 2024 | [حانة] | |
الحساء المتفوق المحلي: حافز لدمج النموذج في التعلم الفدرالي المتقاطع | العصبية | 2024 | [حانة] | |
المتسابق الحرة والتعاون في مجال التعاون للتعلم المتقاطع الفدرالي | العصبية | 2024 | [حانة] | |
تجميع المصنف وتوافق الميزات للتعلم الفدرالي تحت انجراف المفهوم الموزع | العصبية | 2024 | [حانة] | |
أخذ عينات من العميل الموجهة غير المتجهزة: نحو التعلم الفيدرالي سريع وفعال غير IID | العصبية | 2024 | [حانة] | |
حقيقة أو خيال: هل يمكن للآليات الصادقة القضاء على ركوب الخيل الحرة الموحدة؟ | العصبية | 2024 | [حانة] | |
تعلم التفضيل النشط لطلب العناصر داخل وخارج العينة | العصبية | 2024 | [حانة] | |
صياغة دائمة لنماذج اللغة الكبيرة تحت المهام غير المتجانسة وموارد العميل | العصبية | 2024 | [حانة] | |
التخصيص الدقيق في التعلم الفيدرالي لتخفيف العملاء العدوانيين | العصبية | 2024 | [حانة] | |
إعادة النظر في الفرقة في التعلم الفيدرالي واحد | العصبية | 2024 | [حانة] | |
BENTLM-BECK: معايير واقعية للتعلم الفيدرالي لنماذج اللغة الكبيرة | العصبية | 2024 | [حانة] | |
$ exttt {pfl-research} $: إطار محاكاة لتسريع الأبحاث في التعلم الفدرالي الخاص | العصبية | 2024 | [حانة] | |
FedMeki: معيار لتوسيع نطاق نماذج الأساس الطبي عبر حقن المعرفة الموحدة | العصبية | 2024 | [حانة] | |
تقريب الزخم في التعلم الاتحادي الخاص غير المتزامن | ورشة عمل Neupips | 2024 | [حانة] | |
قمة الضغط: ما وراء جولة اتصال واحدة لكل مجموعة في التعلم عبر الجهاز | ورشة عمل Neupips | 2024 | [حانة] | |
التعلم الاتحادي مع المحتوى التوليدي | ورشة عمل Neupips | 2024 | [حانة] | |
الاستفادة من بيانات النص غير المهيكلة لضبط التعليمات الفدرالية لنماذج اللغة الكبيرة | ورشة عمل Neupips | 2024 | [حانة] | |
هجوم السلامة الناشئ والدفاع في توليف تعليمات الاتحادي لنماذج اللغة الكبيرة | ورشة عمل Neupips | 2024 | [حانة] | |
التعاون الخالي من الانشقاق بين المنافسين في نظام التعلم | ورشة عمل Neupips | 2024 | [حانة] | |
على معدلات التقارب من التعلم Q Federated عبر بيئات غير متجانسة | ورشة عمل Neupips | 2024 | [حانة] | |
التشفير: جلب التشفير الوظيفي في النماذج التأسيسية الفيدرالية | ورشة عمل Neupips | 2024 | [حانة] | |
النمس: ضبط المعلمات الكاملة الموحدة على نطاق واسع لنماذج اللغة الكبيرة | ورشة عمل Neupips | 2024 | [حانة] | |
التعلم الفيدرالي القابل للتجميع الساخن | ورشة عمل Neupips | 2024 | [حانة] | |
تدريب ديناميكي منخفض الرتبة مع ضمانات تقارب الخسارة العالمية | ورشة عمل Neupips | 2024 | [حانة] | |
مستقبل نموذج اللغة الكبير قبل التدريب متدرب | ورشة عمل Neupips | 2024 | [حانة] | |
التعلم التعاوني مع التمثيل الخطية المشتركة: معدلات إحصائية وخوارزميات مثالية | ورشة عمل Neupips | 2024 | [حانة] | |
ظاهرة Synapticcity: عندما تتزوج جميع نماذج الأساس | ورشة عمل Neupips | 2024 | [حانة] | |
Zoopfl: استكشاف نماذج الأساس السوداء للتعلم الفدرالي الشخصي | ورشة عمل Neupips | 2024 | [حانة] | |
DECOMFL: التعلم الاتحادي مع التواصل الخالي من الأبعاد | ورشة عمل Neupips | 2024 | [حانة] | |
تحسين اتصال المجموعة لتعميم التعلم العميق الموحدين | ورشة عمل Neupips | 2024 | [حانة] | |
الخريطة: دمج النموذج مع جبهة باريتو المطفأة باستخدام حساب محدود | ورشة عمل Neupips | 2024 | [حانة] | |
OPA: تجميع خاص واحد مع تفاعل عميل واحد وتطبيقاته على التعلم الفدرالي | ورشة عمل Neupips | 2024 | [حانة] | |
النموذج الهجين التكيفي التقليم في التعلم الفدرالي من خلال استكشاف الخسارة | ورشة عمل Neupips | 2024 | [حانة] | |
التدريب الفيدرالي في جميع أنحاء العالم لنماذج اللغة | ورشة عمل Neupips | 2024 | [حانة] | |
Fedstein: تعزيز التعلم متعدد المجالات من خلال مقدر جيمس-ستاين | ورشة عمل Neupips | 2024 | [حانة] | |
تعزيز الاكتشاف السببي في الإعدادات الموحدة مع عينات محلية محدودة | ورشة عمل Neupips | 2024 | [حانة] | |
$ exttt {pfl-research} $: إطار محاكاة لتسريع الأبحاث في التعلم الفدرالي الخاص | ورشة عمل Neupips | 2024 | [حانة] | |
DMM: آلية المصفوفة الموزعة للتعلم الفدرالي ذي القطاع الخاص باستخدام المشاركة السرية المعبأة | ورشة عمل Neupips | 2024 | [حانة] | |
FedCbo: التوصل إلى إجماع المجموعة في التعلم الاتحادي المجمع من خلال التحسين القائم على الإجماع | jmlr | 2024 | [حانة] | |
مطابقة الرسم البياني الفدرالي الفعال | ICML | 2024 | [حانة] | |
فهم التعلم الاتحادي بمساعدة الخادم في وجود مشاركة عميل غير مكتملة | ICML | 2024 | [حانة] | |
ما وراء الاتحاد: تعلم الطوبولوجيا التعلم المودري للتعميم للعملاء غير المرئيين | ICML | 2024 | [حانة] | |
FedBpt: ضبط موجه صناديق سوداء فدرالية فعالة لنماذج اللغة الكبيرة | ICML | 2024 | [حانة] | |
عدم تجانس نموذج سد النموذج في التعلم الفدرالي من خلال التعلم بالمثل غير المتماثل القائم على عدم اليقين | ICML | 2024 | [حانة] | |
منظور نظري جديد حول عدم تجانس البيانات في التحسين الفدرالي | ICML | 2024 | [حانة] | |
تعزيز التخزين والكفاءة الحسابية في التعلم متعدد الوسائط الفيدرالي للنماذج واسعة النطاق | ICML | 2024 | [] | |
زخم للفوز: تعلم التعزيز التعاونية التعاونية عبر بيئات غير متجانسة | ICML | 2024 | [حانة] | |
التعلم الفدرالي البيزنطي-روبوست: تأثير العميل الفرعي والتحديثات المحلية | ICML | 2024 | [حانة] | |
الفوائد التي يمكن إثباتها للخطوات المحلية في التعلم الفدرالي غير المتجانس للشبكات العصبية: منظور تعلم الميزة | ICML | 2024 | [حانة] | |
تسريع التعلم الاتحادي مع تقدير متوسط وموزع سريع | ICML | 2024 | [حانة] | |
التعلم الفيدرالي العادل عبر جوهر الفيتو النسبي | ICML | 2024 | [حانة] | |
AEGISFL: التعلم الفدرالي الكفاءة والمرنة المحفوظة للخصوصية | ICML | 2024 | [حانة] | |
استعادة الملصقات من التحديثات المحلية في التعلم الفيدرالي | ICML | 2024 | [حانة] | |
FedMbridge: التعلم الفيدرالي متعدد الوسائط | ICML | 2024 | [حانة] | |
تنسيق التعميم والتخصيص في التعلم الفوري الموحدة | ICML | 2024 | [حانة] | |
الاضطرابات العالمية المقدرة محليًا أفضل من الاضطرابات المحلية لتقليل الحدة الموحدة إلى الحد الأدنى | ICML | 2024 | [حانة] | |
تسريع التعلم الفيدرالي غير المتجانس مع مصنفات مغلقة | ICML | 2024 | [حانة] | |
المنطقيات متعددة الوكلاء متعددة الوكلاء الموحدة | ICML | 2024 | [حانة] | |
طريقة نزول تدرج تكوين ستوكاستيكي متكرر على نحو مضاعف لتحسين تكوين متعدد المستويات الاتحاد | ICML | 2024 | [حانة] | |
التعلم المودري غير المتجانس الخاص دون إعادة النظر في خادم موثوق به: خوارزميات أخطاء مثالية وفعالة للاتصالات لخسائر محدب | ICML | 2024 | [حانة] | |
FEDRC: معالجة تحديات توزيع متنوعة تحدي في التعلم المودري من خلال التجميع القوي | ICML | 2024 | [حانة] | |
متابعة الرفاهية الشاملة في التعلم الفدرالي من خلال اتخاذ القرارات المتسلسلة | ICML | 2024 | [حانة] | |
ما قبل النص: نماذج لغة التدريب على البيانات الفيدرالية الخاصة في عصر LLMS | ICML | 2024 | [حانة] | |
تجميع الانتروبيا ذاتيا للبيزنطية-روبوست غير المتجانس التعلم الفدرالي | ICML | 2024 | [حانة] | |
التغلب على البيانات وعدم التجانس النموذجي في التعلم الاتحادي اللامركزي عبر المراسي الاصطناعية | ICML | 2024 | [حانة] | |
التحسين المودري مع تصحيح الانجراف المنظم بشكل مضاعف | ICML | 2024 | [حانة] | |
FEDSC: التعلم الخاضع للإشراف ذاتيًا يمكن أن يكون لهدف تباين طيفي على البيانات غير IID | ICML | 2024 | [حانة] | |
على نحو مشهد بيزنطين روبوست تنبؤات مطابقة | ICML | 2024 | [حانة] | |
تحقيق تباين متدرج بدون خسارة من خلال رسم الخرائط إلى مساحة بديلة في التعلم الفدرالي | ICML | 2024 | [حانة] | |
التعلم الاتحادي المجمع عن طريق التقسيم القائم على التدرج | ICML | 2024 | [حانة] | |
المخارج المبكرة المتكررة للتعلم الفدرالي مع العملاء غير المتجانسين | ICML | 2024 | [حانة] | |
إعادة التفكير في الحد الأدنى المسطح في البحث في التعلم الفدرالي | ICML | 2024 | [حانة] | |
FedBat: التعلم الاتحادي فعال التواصل عبر ثنائي القابل للتعلم | ICML | 2024 | [حانة] | |
التعلم التمثيل الفيدرالي في النظام غير المعماري | ICML | 2024 | [حانة] | |
FedLMT: معالجة عدم تجانس النظام من التعلم الفدرالي عبر التدريب النموذجي منخفض الرتبة مع ضمانات نظرية | ICML | 2024 | [حانة] | |
خوارزمية مدركة للضوضاء للتعلم الموحدة الخاص غير المتجانس | ICML | 2024 | [حانة] | |
الفضة: تخفيض التباين أحادي الحلقة والتطبيق على التعلم الفدرالي | ICML | 2024 | [حانة] | |
SignsGD مع الدفاع الفدرالي: تسخير هجمات الخصومة من خلال فك تشفير علامة التدرج | ICML | 2024 | [حانة] | |
FedCal: تحقيق المعايرة المحلية والعالمية في التعلم الفدرالي عبر قائد المعلمات المجمعة | ICML | 2024 | [حانة] | |
التعلم المستمر المتدرب من خلال نقل المعرفة المزدوجة القائمة على الطالبة | ICML | 2024 | [حانة] | |
توليف المعلمة الكاملة الموحدة لنماذج لغة مليار حجم مع تكلفة اتصال أقل من 18 كيلوغرام | ICML | 2024 | [حانة] | |
تعظيم تحت الحواف القابل للتحلل في الإعدادات الموحدة | ICML | 2024 | [حانة] | |
تحسين المحدب العشوائي الخاص والدراسي: استراتيجيات فعالة للأنظمة المركزية | ICML | 2024 | [حانة] | |
تحسين النمذجة لمجموعات البيانات الفيدرالية باستخدام مخاليط من dirichlet-multinomials | ICML | 2024 | [حانة] | |
دروس من تحليل خطأ التعميم للتعلم الفيدرالي: يمكنك التواصل في كثير من الأحيان! | ICML | 2024 | [حانة] | |
بيزنطين مرن ودرسة قليلة الطلقة السريعة | ICML | 2024 | [حانة] | |
التعلم الثابت المخصص للدوافع المخصصة مع التنظيم النظري للمعلومات المختصرة | ICML | 2024 | [حانة] | |
اختيار تقليد العميل القائم على الترتيب للتعلم الفدرالي الفعال | ICML | 2024 | [حانة] | |
نحو نظرية التعلم الفدرالي غير الخاضع للإشراف: التحليل غير المعارض لخوارزميات EM الموحدة | ICML | 2024 | [حانة] | |
FADAS: نحو التحسين غير المتزامن التكيفي الموحدين | ICML | 2024 | [حانة] | |
التعلم التعزيز في وضع عدم الاتصال بالإنترنت: تغطية تعاونية أحادية الجودة تكفي | ICML | 2024 | [حانة] | |
FedRedefense: الدفاع عن هجمات التسمم النموذجية للتعلم الفدرالي باستخدام خطأ إعادة بناء تحديث النموذج | ICML | 2024 | [حانة] | |
MH-PFLID: النموذج غير المتجانس التعلم الاتحادي عبر الحقن والتقطير لتحليل البيانات الطبية | ICML | 2024 | [حانة] | |
التعلم العصبي الفدرالي | ICML | 2024 | [حانة] | |
تخصيص المجموعة التكيفية لتعلم النقل المتبادل الفدرالي | ICML | 2024 | [حانة] | |
موازنة التشابه والتكامل للتعلم الفيدرالي | ICML | 2024 | [حانة] | |
اتباع GNNs ذاتية الفائض مع زيادة مكافحة الشرك | ICML | 2024 | [حانة] | |
خوارزمية الحد الأدنى التكوينية المتعددة المستويات العشوائية الموحدة لتحقيق تعظيم AUC العميق | ICML | 2024 | [حانة] | |
Coala: منصة تعليمية عملية ومتركز على الرؤية | ICML | 2024 | [حانة] | |
التعلم الآمن والسريع غير المتزامن العمودي من خلال التحسين الهجين المتتالي | ماخ تعلم | 2024 | [حانة] | |
التعلم الاتحادي المجمع فعال الاتصال عبر المسافة النموذجية | USTC مختبر الدولة الرئيسي للذكاء المعرفي | ماخ تعلم | 2024 | [حانة] |
التعلم الاتحادي مع التجميع الفائق للبيانات غير المتجانسة. | بحث جوجل | ماخ تعلم | 2024 | [PUB] [PDF] [رمز] |
محاذاة مخرجات النماذج للصف غير المتوازن غير المتوازن في الاتحادي | نيو | ماخ تعلم | 2024 | [حانة] |
التعلم الاتحادي للشبكات السببية الخطية المعممة | tpami | 2024 | [حانة] | |
التعرف على النشاط البشري المتدرج عبر الوسائط | tpami | 2024 | [حانة] | |
عملية غاوسية الاتحادية: التقارب ، التخصيص التلقائي ونمذجة متعددة الأداء | جامعة نورث إيسترن؛ UOM | tpami | 2024 | [PUB] [PDF] [رمز] |
تأثير هجمات الخصومة على التعلم الموحدة: مسح | IIT | tpami | 2024 | [حانة] |
فهم وتخفيف الانهيار الأبعاد في التعلم الفدرالي | جامعة سنغافورة الوطنية | tpami | 2024 | [PUB] [PDF] [رمز] |
لم يترك أحد وراءه: التعلم في العالم الحقيقي | كاس يوكاس | tpami | 2024 | [PUB] [PDF] [رمز] |
التعميم غير المتجانس غير المتجانس على الارتباط المتبادل والتشابه في حالة التشابه | منظمة الصحة العالمية | tpami | 2024 | [PUB] [PDF] [رمز] |
التعلم الاتحادي غير المتزامن متعدد المراحل مع خصوصية تفاضلية تكيفية | HPU ؛ xjtu | tpami | 2024 | [PUB] [PDF] [رمز] |
إطار تعليمي فدرالي بايزي مع تقريب لابلاس على الإنترنت | Sustech | tpami | 2024 | [PUB] [PDF] [رمز] |
تعزيز التعلم الفيدرالي واحد من خلال البيانات والزيادة المشتركة | USTC HKBU | ICLR | 2024 | [حانة] |
تقدير خصوصية تجريبية واحدة للتعلم الفدرالي | جوجل | ICLR | 2024 | [حانة] [PDF] |
متوسط العشوائي الذي يتحكم في المتوسط للتعلم الفيدرالي مع ضغط التواصل | LinkedIn تابن | ICLR | 2024 | [حانة] [PDF] |
طريقة خفيفة الوزن لمعالجة إحصائيات المشاركة غير المعروفة في المتوسط الموحدة | آي بي إم | ICLR | 2024 | [PUB] [PDF] [رمز] |
منظور المعلومات المتبادلة حول التعلم المتناقض الفدرالي | كوالكوم | ICLR | 2024 | [حانة] |
خوارزميات القياس لتعميم المجال الفيدرالي | جامعة بوردو | ICLR | 2024 | [PUB] [PDF] [رمز] |
تعلم الأشجار الفدرالية الفعالة والفعالة على البيانات الهجينة | UC Berkeley | ICLR | 2024 | [حانة] [PDF] |
توصية اتحادية مع التخصيص المضافة | UTS | ICLR | 2024 | [PUB] [PDF] [رمز] |
معالجة عدم تجانس البيانات في التعلم الفدرالي غير المتزامن مع معايرة التحديث المخزنة مؤقتًا | جامعة الأمير سلطان | ICLR | 2024 | [PUB] [SUPR] |
التدريب المتعامد الموحّد: التخفيف من الكارثية النسيان في التعلم الاتحادي المستمر | جامعة جنوب كاليفورنيا | ICLR | 2024 | [PUB] [SUPR] [PDF] |
نسيان دقيق للتعلم المستمر غير المتجانس | الخميس | ICLR | 2024 | [حانة] [رمز] |
الاكتشاف السببي المودري من البيانات غير المتجانسة | mbzuai | ICLR | 2024 | [PUB] [PDF] [رمز] |
على قطاعات السياق الخطي الخطي الخاص تفاضلي | جامعة واين ستيت | ICLR | 2024 | [PUB] [SUPR] [PDF] |
تحفيز التواصل الصادق للقطن اللصوص الفدراليين | جامعة فرجينيا | ICLR | 2024 | [حانة] [PDF] |
تكييف المجال المدني المبدئي: إسقاط التدرج والترويج التلقائي | UIUC | ICLR | 2024 | [حانة] |
FedP3: شبكة شخصية موحدة وصديقة للخصوصية تقليم تحت عدم تجانس النموذج | Kaust | ICLR | 2024 | [حانة] |
جيل موجه مدفوع النص لنماذج لغة الرؤية في التعلم الموحدة | روبرت بوش ذ م | ICLR | 2024 | [حانة] [PDF] |
تحسين لورا في التعلم الفيدرالي للحفاظ على الخصوصية | الجامعة الشمالية الشرقية | ICLR | 2024 | [حانة] |
Fedwon: انتصار التعلم الاتحادي متعدد المجالات دون تطبيع | سوني AI | ICLR | 2024 | [حانة] [PDF] |
FedTrans: تقدير فائدة العميل المنقذ للتعلم الفدرالي القوي | تو دلفت | ICLR | 2024 | [حانة] |
FedCompass: التعلم الفدرالي المتقاطع الفعال على أجهزة العميل غير المتجانسة باستخدام جدولة مدركة للطاقة الحوسبة | ANL. UIUC NCSA | ICLR | 2024 | [PUB] [PDF] [رمز] [صفحة] |
تحسين Coreset Bayesian للتعلم الفيدرالي الشخصي | IIT بومباي | ICLR | 2024 | [حانة] |
اتصال الوضع الخطي بالطبقة | Ruhr-unverstät Bochum | ICLR | 2024 | [PUB] [PDF] [supp] |
مزيف حتى اجعله: التعلم الاتحادي مع جيل موجه نحو الإجماع | Sjtu | ICLR | 2024 | [حانة] [PDF] |
الاختباء في مرأى من البصر: إخفاء البيانات التي تسرق البيانات في التعلم الموحدة | تأكيد | ICLR | 2024 | [PUB] [SUPR] [PDF] |
تحليل الوقت المحدد للتعلم التعزيز المودري غير المتجانس على الجودة | جامعة كولومبيا | ICLR | 2024 | [حانة] [PDF] |
التعلم الاتحادي التكيفي مع العملاء المكرمين تلقائيين | جامعة رايس | ICLR | 2024 | [PUB] [SUPR] [PDF] |
التعلم الفدرالي المتدني عن طريق تسمم الطبقات الناقدة | اختصار الثاني | ICLR | 2024 | [PUB] [SUPR] [PDF] |
تعليم Q Federated: تسريع ندم خطي مع انخفاض تكلفة الاتصال | جامعة الأمير سلطان | ICLR | 2024 | [PUB] [SUPR] [PDF] |
Fedimpro: قياس وتحسين تحديث العميل في التعلم الفدرالي | HKBU | ICLR | 2024 | [حانة] [PDF] |
مسافة WasserStein الفدرالية | معهد ماساتشوستس للتكنولوجيا | ICLR | 2024 | [PUB] [SUPR] [PDF] |
تحليل محسّن للاصطابة لكل عينة و per-avatate في التعلم الفدرالي | DTU | ICLR | 2024 | [حانة] |
FEDCDA: التعلم المودري مع تجميع التباعد المتقاطع | NTU | ICLR | 2024 | [PUB] [SUPR] |
التدرجات الداخلية للطبقة المتقاطعة لتوسيع التجانس إلى عدم التجانس في التعلم الفيدرالي | هوكو | ICLR | 2024 | [حانة] [PDF] |
الزخم يفيد التعلم الفدرالي غير IID ببساطة وجدير | PKU تابن | ICLR | 2024 | [حانة] [PDF] |
التحسين غير الخطي غير الخطي الموفرة للاتصال | جامعة ييل | ICLR | 2024 | [حانة] [PDF] |
تقييم مساهمة عادل وفعال للتعلم العمودي الفدرالي | هواوي | ICLR | 2024 | [Pub] [Supp] [PDF] [رمز] |
إزالة الغموض عن مقايضات الإنصاف المحلية والعالمية في التعلم الفدرالي باستخدام تحلل المعلومات الجزئية | UMCP | ICLR | 2024 | [حانة] [PDF] |
تعلم تمثيلات مخصصة بشكل سببي للعملاء غير المتجانسين | بوليو | ICLR | 2024 | [حانة] |
PEFLL: التعلم الفدرالي المخصص من خلال تعلم التعلم | إست | ICLR | 2024 | [PUB] [SUPR] [PDF] |
أساليب الانتهاك المنحدر المنحدر للاتصالات للاتصالات لعدم المساواة المتغيرة الموزعة: التحليل الموحد والتحديثات المحلية | جو | ICLR | 2024 | [PUB] [SUPR] [PDF] |
FedInverse: تقييم تسرب الخصوصية في التعلم الفدرالي | USQ | ICLR | 2024 | [PUB] [SUPR] |
FEDDA: أساليب التدرج التكيفي الأسرع للتحسين المقيد الفدرالي | UMCP | ICLR | 2024 | [PUB] [SUPR] [PDF] |
التدريب القوي للنماذج الفيدرالية مع نقص العلامات للغاية | HKBU | ICLR | 2024 | [PUB] [PDF] [رمز] |
فهم التقارب والتعميم في التعلم الفدرالي من خلال نظرية تعلم الميزة | Riken AIP | ICLR | 2024 | [حانة] |
تعليم LLMS إلى Phish: سرقة المعلومات الخاصة من نماذج اللغة | جامعة برينستون | ICLR | 2024 | [حانة] |
مثل النفط والماء: لا تختلط أساليب متانة المجموعة ودفاعات التسمم | UMCP | ICLR | 2024 | [حانة] |
تسارع التقارب في طريقة الكرة الثقيلة العشوائية تحت ضوضاء التدرج المتباين الخواص | هكست | ICLR | 2024 | [حانة] [PDF] |
نحو القضاء على قيود التسمية الصعبة في هجمات انعكاس التدرج | CAS | ICLR | 2024 | [Pub] [Supp] [PDF] [رمز] |
تحسين نقطة السرج المركبة المحلية | جامعة بوردو | ICLR | 2024 | [حانة] [PDF] |
تعزيز التدريب العصبي عبر نموذج ديناميات مرتبط | تيت | ICLR | 2024 | [حانة] [PDF] |
econtrol: التحسين الموزعة بسرعة مع الضغط والتحكم في الأخطاء | جامعة سارلاند | ICLR | 2024 | [PUB] [SUPR] [PDF] |
بناء أمثلة خصودية للتعلم الموحدين الرأسي: فساد العميل الأمثل من خلال اللصوص متعددة السلاح | هكست | ICLR | 2024 | [حانة] |
FedHyper: جدولة أسعار تعليمية عالمية وقوية للتعلم الفدرالي مع نزول فرط الدرج | UMCP | ICLR | 2024 | [Pub] [Supp] [PDF] [رمز] |
التعلم الاتحادي المخصص غير المتجانس من خلال تحديثات العظام المحلية التي تخلط عبر معدل التقارب | cuhk | ICLR | 2024 | [حانة] |
Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages | University of Cambridge | ICLR | 2024 | [حانة] |
Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity | NTT DATA Mathematical Systems Inc. | ICLR | 2024 | [حانة] |
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning | الخميس | ICLR | 2024 | [PUB] [PDF] [CODE] |
Incentive-Aware Federated Learning with Training-Time Model Rewards | جامعة سنغافورة الوطنية | ICLR | 2024 | [PUB] [SUPP] |
VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks | جامعة سنغافورة الوطنية | 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 | جامعة روتجرز | 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 | جامعة جنوب كاليفورنيا | 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 | جامعة رايس | 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 | جامعة الأمير سلطان | 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 | [حانة] |
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 | جامعة الأمير سلطان | 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 | قليل | 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 | منظمة الصحة العالمية | 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; تينسنت | 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 | [حانة] |
Breaking the Communication-Privacy-Accuracy Tradeoff with | ZJU | NeurIPS | 2023 | [PUB] [SUPP] [PDF] |
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation | جامعة ستانفورد | 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 | عدة | NeurIPS | 2023 | [PUB] [SUPP] [PDF] [CODE] |
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation | جامعة ستانفورد | 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 | جامعة ستانفورد | 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 | UNC | 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 | [حانة] |
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 | [حانة] | |
HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning | NeurIPS workshop | 2023 | [حانة] | |
Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning | NeurIPS workshop | 2023 | [حانة] | |
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data | NeurIPS workshop | 2023 | [حانة] | |
FedSoL: Bridging Global Alignment and Local Generality in Federated Learning | NeurIPS workshop | 2023 | [حانة] | |
One-shot Empirical Privacy Estimation for Federated Learning | NeurIPS workshop | 2023 | [حانة] | |
Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning | NeurIPS workshop | 2023 | [حانة] | |
SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models | NeurIPS workshop | 2023 | [حانة] | |
The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning | NeurIPS workshop | 2023 | [حانة] | |
Towards Building the FederatedGPT: Federated Instruction Tuning | NeurIPS workshop | 2023 | [حانة] | |
Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR | NeurIPS workshop | 2023 | [حانة] | |
LASER: Linear Compression in Wireless Distributed Optimization | NeurIPS workshop | 2023 | [حانة] | |
MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization | NeurIPS workshop | 2023 | [حانة] | |
TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation | NeurIPS workshop | 2023 | [حانة] | |
An Empirical Evaluation of Federated Contextual Bandit Algorithms | NeurIPS workshop | 2023 | [حانة] | |
RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation | NeurIPS workshop | 2023 | [حانة] | |
FDAPT: Federated Domain-adaptive Pre-training for Language Models | NeurIPS workshop | 2023 | [حانة] | |
Making Batch Normalization Great in Federated Deep Learning | NeurIPS workshop | 2023 | [حانة] | |
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning | NeurIPS workshop | 2023 | [حانة] | |
Parameter Averaging Laws for Multitask Language Models | NeurIPS workshop | 2023 | [حانة] | |
Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages | NeurIPS workshop | 2023 | [حانة] | |
Beyond Parameter Averaging in Model Aggregation | NeurIPS workshop | 2023 | [حانة] | |
Augmenting Federated Learning with Pretrained Transformers | NeurIPS workshop | 2023 | [حانة] | |
Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization | NeurIPS workshop | 2023 | [حانة] | |
DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization | NeurIPS workshop | 2023 | [حانة] | |
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning | NeurIPS workshop | 2023 | [حانة] | |
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System | NeurIPS workshop | 2023 | [حانة] | |
Learning Optimizers for Local SGD | NeurIPS workshop | 2023 | [حانة] | |
Exploring User-level Gradient Inversion with a Diffusion Prior | NeurIPS workshop | 2023 | [حانة] | |
User Inference Attacks on Large Language Models | NeurIPS workshop | 2023 | [حانة] | |
FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis | NeurIPS workshop | 2023 | [حانة] | |
Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models | NeurIPS workshop | 2023 | [حانة] | |
Backdoor Threats from Compromised Foundation Models to Federated Learning | NeurIPS workshop | 2023 | [حانة] | |
MOFL/D: A Federated Multi-objective Learning Framework with Decomposition | NeurIPS workshop | 2023 | [حانة] | |
Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning | NeurIPS workshop | 2023 | [حانة] | |
Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models | NeurIPS workshop | 2023 | [حانة] | |
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning | NeurIPS workshop | 2023 | [حانة] | |
Private and Personalized Histogram Estimation in a Federated Setting | NeurIPS workshop | 2023 | [حانة] | |
The Aggregation–Heterogeneity Trade-off in Federated Learning | PKU | COLT | 2023 | [حانة] |
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 | مجموعة علي بابا | ICML | 2023 | [PUB] [PDF] [CODE] |
Federated Conformal Predictors for Distributed Uncertainty Quantification | معهد ماساتشوستس للتكنولوجيا | 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 | جامعة كاليفورنيا | 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 | [حانة] |
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 | جامعة سنغافورة الوطنية | 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 | [حانة] |
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 | جامعة كاليفورنيا | 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; جوجل | ICML | 2023 | [PUB] [PDF] |
Fast Federated Machine Unlearning with Nonlinear Functional Theory | جامعة أوبورن | ICML | 2023 | [حانة] |
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 | [حانة] |
Flash: Concept Drift Adaptation in Federated Learning | University of Massachusetts | ICML | 2023 | [حانة] |
DoCoFL: Downlink Compression for Cross-Device Federated Learning | VMware Research; Technion | ICML | 2023 | [PUB] [PDF] |
FeDXL: Provable Federated Learning for Deep X-Risk Optimization | جامعة تكساس ايه اند ام | ICML | 2023 | [PUB] [PDF] [CODE] |
No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation | يضرب | 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 | ميتا الذكاء الاصطناعي | 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 | الخميس | ICML | 2023 | [PUB] [PDF] |
Efficient Personalized Federated Learning via Sparse Model-Adaptation | مجموعة علي بابا | ICML | 2023 | [PUB] [PDF] [CODE] |
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning | جامعة. ليل | 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; ميتا الذكاء الاصطناعي | 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 | جامعة سنغافورة الوطنية | 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; جامعة سنغافورة الوطنية | ICML | 2023 | [حانة] |
XTab: Cross-table Pretraining for Tabular Transformers | EPFL; Cornell University; أوس | ICML | 2023 | [PUB] [PDF] [CODE] |
Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions | NUDT | ICML | 2023 | [حانة] |
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 | جامعة مينيسوتا | ICML | 2023 | [حانة] |
Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm | جامعة شيكاغو | 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 | [حانة] |
Robust federated learning under statistical heterogeneity via hessian-weighted aggregation | Deakin University | Mach Learn | 2023 | [حانة] |
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 | [حانة] | |
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 | الخميس | ICLR | 2023 | [PUB] [CODE] |
MocoSFL: enabling cross-client collaborative self-supervised learning | جامعة ولاية أريزونا | ICLR | 2023 | [PUB] [CODE] |
Single-shot General Hyper-parameter Optimization for Federated Learning | آي بي إم | ICLR | 2023 | [PUB] [PDF] [CODE] |
Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated | فيسبوك | 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 | جامعة ولاية ميشيغان | 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 | جامعة سنغافورة الوطنية | 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 | [حانة] |
Towards Addressing Label Skews in One-Shot Federated Learning | جامعة سنغافورة الوطنية | ICLR | 2023 | [PUB] [CODE] |
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning | جامعة سنغافورة الوطنية | 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 | جامعة جنوب كاليفورنيا | ICLR | 2023 | [PUB] [PDF] [CODE] |
Effective passive membership inference attacks in federated learning against overparameterized models | Purdue University | ICLR | 2023 | [حانة] |
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 | الخميس | 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 | هارفارد | 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 | [حانة] |
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 | جامعة كاليفورنيا | 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 | [حانة] |
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 | ستانفورد | 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? | معهد ماساتشوستس للتكنولوجيا | 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 | ستانفورد | 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. | ميتا الذكاء الاصطناعي | 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 | جامعة سنغافورة الوطنية | UAI | 2022 | [PUB] [PDF] |
Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning | Hanyang University | TPAMI | 2022 | [حانة] |
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 | جامعة كولومبيا | NeurIPS | 2022 | [PUB] [PDF] |
Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective | PKU | NeurIPS | 2022 | [حانة] |
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise | ستانفورد | 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 | هواوي | 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 | جامعة ولاية ميشيغان | NeurIPS | 2022 | [PUB] [CODE] |
On Sample Optimality in Personalized Collaborative and Federated Learning | INRIA | NeurIPS | 2022 | [حانة] |
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 | الخميس | NeurIPS | 2022 | [حانة] |
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? | منظمة الصحة العالمية | 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 | [حانة] |
Federated Submodel Optimization for Hot and Cold Data Features | SJTU | NeurIPS | 2022 | [حانة] |
BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels | UC Berkeley | NeurIPS | 2022 | [PUB] [PDF] |
Byzantine-tolerant federated Gaussian process regression for streaming data | جامعة الأمير سلطان | 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 | ييل | 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 | [حانة] |
Self-Aware Personalized Federated Learning | أمازون | 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 | جامعة سنغافورة الوطنية | NeurIPS | 2022 | [حانة] |
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 | [حانة] |
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 | آي بي إم | 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 | جامعة كاليفورنيا | NeurIPS | 2022 | [PUB] [PDF] |
Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework | Tulane University | NeurIPS | 2022 | [حانة] |
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 | جامعة سنغافورة الوطنية | 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 | جامعة أوبورن | 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 | جامعة ميشيغان | 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 | جامعة ميشيغان | 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 | University of 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 | جامعة ميشيغان | 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 | CAS | ICML | 2022 | [PUB] [PDF] [SLIDE] [UC.] |
Federated Learning with Label Distribution Skew via Logits Calibration | ZJU | ICML | 2022 | [حانة] |
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 | [حانة] |
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; University of Washington | ICLR | 2022 | [PUB] [PDF] |
Acceleration of Federated Learning with Alleviated Forgetting in Local Training | الخميس | 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 | جامعة بنسلفانيا | 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? | بوردو | ICLR | 2022 | [PUB] [PDF] [CODE] |
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions | University of Maryland; جوجل | 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; جوجل | 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 | [حانة] |
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; معهد ماساتشوستس للتكنولوجيا | 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; ذراع | 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; جوجل | ICLR | 2021 | [PUB] [PDF] [CODE] |
Adaptive Federated Optimization | جوجل | ICLR | 2021 | [PUB] [PDF] [CODE] |
Personalized Federated Learning with First Order Model Optimization | Stanford; نفيديا | 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; نفيديا | ICML | 2021 | [PUB] [PDF] [CODE] [PAGE] [VIDEO] [解读] |
Federated Composite Optimization | Stanford; جوجل | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] [SLIDE] |
Exploiting Shared Representations for Personalized Federated Learning | University of Texas at Austin; جامعة بنسلفانيا | 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 | كوالكوم | ICML | 2021 | [PUB] [PDF] [VIDEO] |
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning | أكسنتشر | 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 | جوجل | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
Debiasing Model Updates for Improving Personalized Federated Training | BU; ذراع | 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; آي بي إم | ICML | 2021 | [PUB] [PDF] [CODE] [VIDEO] |
Federated Learning under Arbitrary Communication Patterns | Indiana University; أمازون | ICML | 2021 | [PUB] [VIDEO] |
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression | CMU | NeurIPS | 2021 | [PUB] [PDF] |
Boosting with Multiple Sources | جوجل | NeurIPS | 2021 | [حانة] |
DRIVE: One-bit Distributed Mean Estimation | إم وير | NeurIPS | 2021 | [PUB] [CODE] |
Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning | جامعة سنغافورة الوطنية | 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 | جامعة كاليفورنيا | NeurIPS | 2021 | [PUB] [PDF] |
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries | KAIST | NeurIPS | 2021 | [حانة] |
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 | جامعة سنغافورة الوطنية | 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 | جامعة كاليفورنيا | 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; هواوي | 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; جامعة سنغافورة الوطنية | 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; معهد ماساتشوستس للتكنولوجيا | 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 | جامعة بنسلفانيا | 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 | إيموري | NeurIPS | 2021 | [PUB] [PDF] [CODE] [解读] |
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing | CMU; شركة هيوليت باكارد | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
On Large-Cohort Training for Federated Learning | جوجل؛ 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 | هواوي | 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; فيسبوك؛ University of Virginia | NeurIPS | 2021 | [PUB] [PDF] [CODE] |
Few-Round Learning for Federated Learning | KAIST | NeurIPS | 2021 | [حانة] |
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; جوجل | 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; جامعة روتجرز | 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 | جوجل | 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 | جوجل | ICML | 2020 | [PUB] [VIDEO] |
FetchSGD: Communication-Efficient Federated Learning with Sketching | UC Berkeley; Johns Hopkins University; أمازون | ICML | 2020 | [PUB] [PDF] [VIDEO] [CODE] |
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning | EPFL; جوجل | ICML | 2020 | [PUB] [PDF] [VIDEO] [UC.] [解读] |
Federated Learning with Only Positive Labels | جوجل | 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 | معهد ماساتشوستس للتكنولوجيا | 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; معهد ماساتشوستس للتكنولوجيا | 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; معهد ماساتشوستس للتكنولوجيا | 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 | جامعة جنوب كاليفورنيا | 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 | ستانفورد | 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 | آي بي إم | ICML | 2019 | [PUB] [PDF] [CODE] |
Analyzing Federated Learning through an Adversarial Lens | Princeton; آي بي إم | ICML | 2019 | [PUB] [PDF] [CODE] |
Agnostic Federated Learning | جوجل | ICML | 2019 | [PUB] [PDF] |
cpSGD: Communication-efficient and differentially-private distributed SGD | Princeton; جوجل | 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).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics | KDD Workshop | 2024 | [حانة] | |
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination | KDD | 2024 | [حانة] | |
BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning | KDD | 2024 | [حانة] | |
Federated Graph Learning with Structure Proxy Alignment | KDD | 2024 | [حانة] | |
HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning | KDD | 2024 | [حانة] | |
FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs | KDD | 2024 | [حانة] | |
Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization | KDD | 2024 | [حانة] | |
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning | KDD | 2024 | [حانة] | |
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models | KDD | 2024 | [حانة] | |
CASA: Clustered Federated Learning with Asynchronous Clients | KDD | 2024 | [حانة] | |
FLAIM: AIM-based Synthetic Data Generation in the Federated Setting | KDD | 2024 | [حانة] | |
Privacy-Preserving Federated Learning using Flower Framework | KDD | 2024 | [حانة] | |
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning | KDD | 2024 | [حانة] | |
FedNLR: Federated Learning with Neuron-wise Learning Rates | KDD | 2024 | [حانة] | |
FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model | KDD | 2024 | [حانة] | |
FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation | KDD | 2024 | [حانة] | |
Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning | KDD | 2024 | [حانة] | |
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection | KDD | 2024 | [حانة] | |
FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation | KDD | 2024 | [حانة] | |
FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction | KDD | 2024 | [حانة] | |
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning | KDD | 2024 | [حانة] | |
Personalized Federated Continual Learning via Multi-Granularity Prompt | KDD | 2024 | [حانة] | |
Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning | KDD | 2024 | [حانة] | |
GPFedRec: Graph-Guided Personalization for Federated Recommendation | KDD | 2024 | [حانة] | |
Asynchronous Vertical Federated Learning for Kernelized AUC Maximization | KDD | 2024 | [حانة] | |
VertiMRF: Differentially Private Vertical Federated Data Synthesis | KDD | 2024 | [حانة] | |
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 | [حانة] |
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 | [حانة] |
Personalized Federated Learning with Parameter Propagation | UIUC | KDD | 2023 | [حانة] |
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 | مجموعة علي بابا | KDD | 2023 | [PUB] [PDF] |
FedMultimodal: A Benchmark for Multimodal Federated Learning | جامعة جنوب كاليفورنيا | 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; مجموعة علي بابا | 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 | جامعة ولاية ميشيغان | KDD Workshop Summaries | 2023 | [PUB] [PAGE] |
Is Normalization Indispensable for Multi-domain Federated Learning? | KDD workshop | 2023 | [حانة] | |
Distributed Personalized Empirical Risk Minimization. | KDD workshop | 2023 | [حانة] | |
Once-for-All Federated Learning: Learning From and Deploying to Heterogeneous Clients. | KDD workshop | 2023 | [حانة] | |
SparseVFL: Communication-Efficient Vertical Federated Learning Based on Sparsification of Embeddings and Gradients. | KDD workshop | 2023 | [حانة] | |
Optimization of User Resources in Federated Learning for Urban Sensing Applications | KDD workshop | 2023 | [حانة] | |
FedLEGO: Enabling Heterogenous Model Cooperation via Brick Reassembly in Federated Learning. | KDD workshop | 2023 | [حانة] | |
Federated Graph Analytics with Differential Privacy. | KDD workshop | 2023 | [حانة] | |
Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. | KDD workshop | 2023 | [حانة] | |
Uncertainty Quantification in Federated Learning for Heterogeneous Health Data | KDD workshop | 2023 | [حانة] | |
A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing. | KDD workshop | 2023 | [حانة] | |
Taming Heterogeneity to Deal with Test-Time Shift in Federated Learning. | KDD workshop | 2023 | [حانة] | |
Federated Blood Supply Chain Demand Forecasting: A Case Study. | KDD workshop | 2023 | [حانة] | |
Stochastic Clustered Federated Learning. | KDD workshop | 2023 | [حانة] | |
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection. | KDD workshop | 2023 | [حانة] | |
Exploring the Efficacy of Data-Decoupled Federated Learning for Image Classification and Medical Imaging Analysis. | KDD workshop | 2023 | [حانة] | |
FedNoisy: A Federated Noisy Label Learning Benchmark | KDD workshop | 2023 | [حانة] | |
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging | KDD workshop | 2023 | [حانة] | |
Federated learning for competing risk analysis in healthcare. | KDD workshop | 2023 | [حانة] | |
Federated Threat Detection for Smart Home IoT rules. | KDD workshop | 2023 | [حانة] | |
Federated Unlearning for On-Device Recommendation | UQ | WSDM | 2023 | [PUB] [PDF] |
Collaboration Equilibrium in Federated Learning | الخميس | 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 | [حانة] |
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 | علي بابا | 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 | الخميس | 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 | جامعة جنوب كاليفورنيا | 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).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
Byzantine-Robust Decentralized Federated Learning | احتجاز ثاني أكسيد الكربون | 2024 | [حانة] | |
Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation | احتجاز ثاني أكسيد الكربون | 2024 | [حانة] | |
Cross-silo Federated Learning with Record-level Personalized Differential Privacy. | احتجاز ثاني أكسيد الكربون | 2024 | [حانة] | |
Samplable Anonymous Aggregation for Private Federated Data Analysis | احتجاز ثاني أكسيد الكربون | 2024 | [حانة] | |
Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy | احتجاز ثاني أكسيد الكربون | 2024 | [حانة] | |
Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses | احتجاز ثاني أكسيد الكربون | 2024 | [حانة] | |
Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning. | احتجاز ثاني أكسيد الكربون | 2024 | [حانة] | |
Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling. | احتجاز ثاني أكسيد الكربون | 2024 | [حانة] | |
Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration. | احتجاز ثاني أكسيد الكربون | 2024 | [حانة] | |
FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting | NDSS | 2024 | [حانة] | |
FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning | NDSS | 2024 | [حانة] | |
Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning | NDSS | 2024 | [حانة] | |
CrowdGuard: Federated Backdoor Detection in Federated Learning | NDSS | 2024 | [حانة] | |
Protecting Label Distribution in Cross-Silo Federated Learning | S&P | 2024 | [حانة] | |
FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks | S&P | 2024 | [حانة] | |
BadVFL: Backdoor Attacks in Vertical Federated Learning | S&P | 2024 | [حانة] | |
SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks | S&P | 2024 | [حانة] | |
Loki: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation | S&P | 2024 | [حانة] | |
Poster: Towards Privacy-Preserving Federated Recommendation via Synthetic Interactions. | S&P Workshop | 2024 | [حانة] | |
A Performance Analysis for Confidential Federated Learning. | S&P Workshop | 2024 | [حانة] | |
Turning Privacy-preserving Mechanisms against Federated Learning | University of Pavia | احتجاز ثاني أكسيد الكربون | 2023 | [PUB] [PDF] |
MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers | University of Würzburg | احتجاز ثاني أكسيد الكربون | 2023 | [حانة] |
martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture | الخميس | احتجاز ثاني أكسيد الكربون | 2023 | [PUB] [PDF] [CODE] |
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks | UIUC | احتجاز ثاني أكسيد الكربون | 2023 | [PUB] [PDF] |
Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning | NSYSU | احتجاز ثاني أكسيد الكربون | 2023 | [حانة] |
Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems | RWTH Aachen University | احتجاز ثاني أكسيد الكربون | 2023 | [حانة] |
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 | احتجاز ثاني أكسيد الكربون | 2023 | [PUB] [PDF] [CODE] |
CERBERUS: Exploring Federated Prediction of Security Events | UCL London | احتجاز ثاني أكسيد الكربون | 2022 | [PUB] [PDF] |
EIFFeL: Ensuring Integrity for Federated Learning | UW-Madison | احتجاز ثاني أكسيد الكربون | 2022 | [PUB] [PDF] |
Eluding Secure Aggregation in Federated Learning via Model Inconsistency | SPRING Lab; EPFL | احتجاز ثاني أكسيد الكربون | 2022 | [PUB] [PDF] [CODE] |
Federated Boosted Decision Trees with Differential Privacy | University of Warwick | احتجاز ثاني أكسيد الكربون | 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 | [حانة] |
On the Pitfalls of Security Evaluation of Robust Federated Learning. | umass | S&P Workshop | 2023 | [حانة] |
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 | جامعة فودان | 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 | جامعة سنغافورة الوطنية | احتجاز ثاني أكسيد الكربون | 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 | [حانة] |
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 | جامعة كانساس | CCS (Poster) | 2019 | [حانة] |
IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning | Université du Québéc á Montréal | S&P Workshop | 2019 | [حانة] |
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 | جوجل | احتجاز ثاني أكسيد الكربون | 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).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations | مم | 2024 | [حانة] | |
One-shot-but-not-degraded Federated Learning | مم | 2024 | [حانة] | |
Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning | مم | 2024 | [حانة] | |
FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models | مم | 2024 | [حانة] | |
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition | مم | 2024 | [حانة] | |
CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation | مم | 2024 | [حانة] | |
Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training | مم | 2024 | [حانة] | |
FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework | مم | 2024 | [حانة] | |
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity | مم | 2024 | [حانة] | |
FedSLS: Exploring Federated Aggregation in Saliency Latent Space | مم | 2024 | [حانة] | |
Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia | مم | 2024 | [حانة] | |
FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning | مم | 2024 | [حانة] | |
Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data | مم | 2024 | [حانة] | |
Cross-Modal Meta Consensus for Heterogeneous Federated Learning | مم | 2024 | [حانة] | |
Masked Random Noise for Communication-Efficient Federated Learning | مم | 2024 | [حانة] | |
Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations | مم | 2024 | [حانة] | |
Adaptive Hierarchical Aggregation for Federated Object Detection | مم | 2024 | [حانة] | |
FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement | مم | 2024 | [حانة] | |
Federated Fuzzy C-means with Schatten-p Norm Minimization | مم | 2024 | [حانة] | |
Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation | مم | 2024 | [حانة] | |
Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification | IJCV | 2024 | [حانة] | |
FedHide: Federated Learning by Hiding in the Neighbors | ECCV | 2024 | [حانة] | |
FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation | ECCV | 2024 | [حانة] | |
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients | ECCV | 2024 | [حانة] | |
Pick-a-Back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning | ECCV | 2024 | [حانة] | |
Federated Learning with Local Openset Noisy Labels | ECCV | 2024 | [حانة] | |
FedTSA: A Cluster-Based Two-Stage Aggregation Method for Model-Heterogeneous Federated Learning. | ECCV | 2024 | [حانة] | |
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection | ECCV | 2024 | [حانة] | |
BAFFLE: A Baseline of Backpropagation-Free Federated Learning | ECCV | 2024 | [حانة] | |
PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning | ECCV | 2024 | [حانة] | |
Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning | ECCV | 2024 | [حانة] | |
Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents | ECCV | 2024 | [حانة] | |
FedHARM: Harmonizing Model Architectural Diversity in Federated Learning | ECCV | 2024 | [حانة] | |
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. | ECCV | 2024 | [حانة] | |
Personalized Federated Domain-Incremental Learning Based on Adaptive Knowledge Matching. | ECCV | 2024 | [حانة] | |
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning | ECCV | 2024 | [حانة] | |
Towards Multi-modal Transformers in Federated Learning | ECCV | 2024 | [حانة] | |
Local and Global Flatness for Federated Domain Generalization | ECCV | 2024 | [حانة] | |
Feature Diversification and Adaptation for Federated Domain Generalization | ECCV | 2024 | [حانة] | |
PFEDEDIT: Personalized Federated Learning via Automated Model Editing | ECCV | 2024 | [حانة] | |
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 | منظمة الصحة العالمية | 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 | يوتا | CVPR | 2024 | [PUB] [SUPP] [PDF] |
Data Valuation and Detections in Federated Learning | جامعة سنغافورة الوطنية | 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 | جامعة بولونيا | 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 | اختصار الثاني | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
Text-Enhanced Data-free Approach for Federated Class-Incremental Learning | Monash University | CVPR | 2024 | [PUB] [SUPP] [PDF] [CODE] |
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees | UIUC; نفيديا | 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 | منظمة الصحة العالمية | 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 | [حانة] | |
On the Efficiency of Privacy Attacks in Federated Learning | CVPR workshop | 2024 | [PUB] [PDF] | |
FedCE: Personalized Federated Learning Method based on Clustering Ensembles | BJTU | مم | 2023 | [حانة] |
FedVQA: Personalized Federated Visual Question Answering over Heterogeneous Scenes | Leiden University | مم | 2023 | [حانة] |
Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor | XJTU | مم | 2023 | [PUB] [PDF] [CODE] |
Federated Deep Multi-View Clustering with Global Self-Supervision | UESTC | مم | 2023 | [PUB] [PDF] |
FedAA: Using Non-sensitive Modalities to Improve Federated Learning while Preserving Image Privacy | ZJU | مم | 2023 | [حانة] |
Prototype-guided Knowledge Transfer for Federated Unsupervised Cross-modal Hashing | SDNU | مم | 2023 | [PUB] [CODE] |
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data | ZJU | مم | 2023 | [PUB] [PDF] |
FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data | BUPT | مم | 2023 | [حانة] |
Federated Learning with Label-Masking Distillation | يوكاس | مم | 2023 | [PUB] [CODE] |
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data | SDU | مم | 2023 | [PUB] [PDF] [CODE] |
A Four-Pronged Defense Against Byzantine Attacks in Federated Learning | HUST | مم | 2023 | [PUB] [PDF] |
Client-Adaptive Cross-Model Reconstruction Network for Modality-Incomplete Multimodal Federated Learning | CAS; Peng Cheng Laboratory; يوكاس | مم | 2023 | [حانة] |
FedGH: Heterogeneous Federated Learning with Generalized Global Header | NKU | مم | 2023 | [PUB] [PDF] [CODE] |
Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation | CUHK | مم | 2023 | [PUB] [PDF] [CODE] |
AffectFAL: Federated Active Affective Computing with Non-IID Data | TJUT | مم | 2023 | [PUB] [CODE] |
Improving Federated Person Re-Identification through Feature-Aware Proximity and Aggregation | SZU | مم | 2023 | [حانة] |
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; نفيديا | 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 | ذنيب ذنب قصير | 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 | جامعة رايس | ICCV | 2023 | [PUB] [PDF] [CODE] [SUPP] |
Robust Heterogeneous Federated Learning under Data Corruption | منظمة الصحة العالمية | 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 | [حانة] |
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 | [حانة] |
Rethinking Federated Learning With Domain Shift: A Prototype View | منظمة الصحة العالمية | 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 | [حانة] |
On the Effectiveness of Partial Variance Reduction in Federated Learning With Heterogeneous Data | DTU | CVPR | 2023 | [PUB] [PDF] |
Elastic Aggregation for Federated Optimization | ميتوان | CVPR | 2023 | [حانة] |
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning | جامعة كاليفورنيا | CVPR | 2023 | [PUB] [PDF] |
Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity | أم | CVPR | 2023 | [حانة] |
ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients | GaTech | CVPR | 2023 | [PUB] [CODE] |
Reliable and Interpretable Personalized Federated Learning | TJU | CVPR | 2023 | [حانة] |
Federated Domain Generalization With Generalization Adjustment | SJTU | CVPR | 2023 | [PUB] [CODE] |
Make Landscape Flatter in Differentially Private Federated Learning | الخميس | 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 | [حانة] |
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 | [حانة] |
Federated Incremental Semantic Segmentation | CAS; يوكاس | 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 | ميتوان | 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 | جامعة جنوب كاليفورنيا | 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; يوكاس | مم | 2022 | [حانة] |
Few-Shot Model Agnostic Federated Learning | منظمة الصحة العالمية | مم | 2022 | [PUB] [CODE] |
Feeling Without Sharing: A Federated Video Emotion Recognition Framework Via Privacy-Agnostic Hybrid Aggregation | TJUT | مم | 2022 | [حانة] |
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 | جامعة شيامن | ECCV | 2022 | [PUB] [SUPP] [PDF] [CODE] |
ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning Framework | يضرب | CVPR | 2022 | [حانة] |
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning | ستانفورد | 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 | CAS | 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 | جامعة. of Pittsburgh; نفيديا | CVPR | 2022 | [PUB] [PDF] |
Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning | مرحباً | 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 | [حانة] |
Does Federated Dropout Actually Work? | ستانفورد | 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 | جامعة جونز هوبكنز | 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 | [حانة] |
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 | مم | 2021 | [PUB] [PDF] |
Federated Visual Classification with Real-World Data Distribution | MIT; جوجل | ECCV | 2020 | [PUB] [PDF] [VIDEO] |
InvisibleFL: Federated Learning over Non-Informative Intermediate Updates against Multimedia Privacy Leakages | مم | 2020 | [حانة] | |
Performance Optimization of Federated Person Re-identification via Benchmark Analysis data. | NTU | مم | 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).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems | EMNLP | 2024 | [حانة] | |
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model | EMNLP | 2024 | [حانة] | |
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models | EMNLP | 2024 | [حانة] | |
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models | EMNLP | 2024 | [حانة] | |
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models | EMNLP | 2024 | [حانة] | |
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA | EMNLP Findings | 2024 | [حانة] | |
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models | EMNLP Findings | 2024 | [حانة] | |
Generalizable Multilingual Hate Speech Detection on Low Resource Indian Languages using Fair Selection in Federated Learning | NAACL | 2024 | [حانة] | |
Open-Vocabulary Federated Learning with Multimodal Prototyping | NAACL | 2024 | [حانة] | |
Navigation as Attackers Wish? Towards Building Robust Embodied Agents under Federated Learning | NAACL | 2024 | [حانة] | |
FedLFC: Towards Efficient Federated Multilingual Modeling with LoRA-based Language Family Clustering. | NAACL Findings | 2024 | [حانة] | |
Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning. | NAACL Findings | 2024 | [حانة] | |
Can Public Large Language Models Help Private Cross-device Federated Learning? | NAACL Findings | 2024 | [حانة] | |
Fair Federated Learning with Biased Vision-Language Models | ACL Findings | 2024 | [حانة] | |
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization | جامعة أوبورن | 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 | الرباط الصليبي الأمامي | 2023 | [PUB] [PDF] [CODE] |
FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP | يضرب؛ Peng Cheng Lab | الرباط الصليبي الأمامي | 2023 | [PUB] [CODE] |
Client-Customized Adaptation for Parameter-Efficient Federated Learning | ACL Findings | 2023 | [حانة] | |
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 | [حانة] | |
FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models | ACL Findings | 2023 | [حانة] | |
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 | جامعة ألبرتا | EMNLP | 2022 | [PUB] [PDF] [CODE] |
Federated Model Decomposition with Private Vocabulary for Text Classification | يضرب؛ Peng Cheng Lab | EMNLP | 2022 | [PUB] [CODE] |
Fair NLP Models with Differentially Private Text Encoders | جامعة. ليل | 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 | جوجل | ACL workshop | 2022 | [PUB] [PDF] |
Intrinsic Gradient Compression for Scalable and Efficient Federated Learning | أكسفورد | ACL workshop | 2022 | [PUB] [PDF] |
ActPerFL: Active Personalized Federated Learning | أمازون | ACL workshop | 2022 | [PUB] [PAGE] |
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks | جامعة جنوب كاليفورنيا | NAACL | 2022 | [PUB] [PDF] [CODE] |
Federated Learning with Noisy User Feedback | USC; أمازون | NAACL | 2022 | [PUB] [PDF] |
Training Mixed-Domain Translation Models via Federated Learning | أمازون | NAACL | 2022 | [PUB] [PAGE] [PDF] |
Pretrained Models for Multilingual Federated Learning | جامعة جونز هوبكنز | NAACL | 2022 | [PUB] [PDF] [CODE] |
Federated Chinese Word Segmentation with Global Character Associations | University of 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 | University of Connecticut | EMNLP | 2021 | [PUB] [PDF] [VIDEO] |
Distantly Supervised Relation Extraction in Federated Settings | يوكاس | EMNLP workshop | 2021 | [PUB] [PDF] [CODE] |
Federated Learning with Noisy User Feedback | USC; أمازون | NAACL workshop | 2021 | [PUB] [PDF] |
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework | Universität Hamburg | NAACL workshop | 2021 | [حانة] |
Understanding Unintended Memorization in Language Models Under Federated Learning | جوجل | NAACL workshop | 2021 | [PUB] [PDF] |
FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction | CAS | EMNLP | 2020 | [PUB] [VIDEO] [解读] |
Empirical Studies of Institutional Federated Learning For Natural Language Processing | Ping An Technology | EMNLP workshop | 2020 | [حانة] |
Federated Learning for Spoken Language Understanding | PKU | COLING | 2020 | [حانة] |
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).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
ReFer: Retrieval-Enhanced Vertical Federated Recommendation for Full Set User Benefit | الخميس | SIGIR | 2024 | [حانة] |
Revisit Targeted Model Poisoning on Federated Recommendation: Optimize via Multi-objective Transport | ZJU | SIGIR | 2024 | [حانة] |
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 | مجموعة علي بابا | SIGIR | 2024 | [حانة] |
Personalized Federated Relation Classification over Heterogeneous Texts | NUDT | SIGIR | 2023 | [حانة] |
Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity | SDU | SIGIR | 2023 | [حانة] |
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 | مجموعة علي بابا | 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 | [حانة] |
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 | جامعة روتجرز | SIGIR | 2021 | [PUB] [PDF] [CODE] |
On the Privacy of Federated Pipelines | الجامعة التقنية في ميونيخ | SIGIR | 2021 | [حانة] |
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).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
FedMix: Boosting with Data Mixture for Vertical Federated Learning | ICDE | 2024 | [حانة] | |
FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation | ICDE | 2024 | [حانة] | |
Clients Help Clients: Alternating Collaboration for Semi-Supervised Federated Learning | ICDE | 2024 | [حانة] | |
Semi-Asynchronous Online Federated Crowdsourcing | ICDE | 2024 | [حانة] | |
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity | ICDE | 2024 | [حانة] | |
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation | ICDE | 2024 | [حانة] | |
LightTR: A Lightweight Framework for Federated Trajectory Recovery | ICDE | 2024 | [حانة] | |
Feed: Towards Personalization-Effective Federated Learning | ICDE | 2024 | [حانة] | |
Label Noise Correction for Federated Learning: A Secure, Efficient and Reliable Realization | ICDE | 2024 | [حانة] | |
Fast, Robust and Interpretable Participant Contribution Estimation for Federated Learning | ICDE | 2024 | [حانة] | |
HeteFedRec: Federated Recommender Systems with Model Heterogeneity | ICDE | 2024 | [حانة] | |
Hide Your Model: A Parameter Transmission-free Federated Recommender System | ICDE | 2024 | [حانة] | |
FedCTQ: A Federated-Based Framework for Accurate and Efficient Contact Tracing Query | ICDE | 2024 | [حانة] | |
Preventing the Popular Item Embedding Based Attack in Federated Recommendations | ICDE | 2024 | [حانة] | |
RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection | ICDE | 2024 | [حانة] | |
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly | توم | DEEM@SIGMOD | 2024 | [حانة] |
FedSQ: A Secure System for Federated Vector Similarity Queries | VLDB | 2024 | [حانة] | |
FedSM: A Practical Federated Shared Mobility System | VLDB | 2024 | [حانة] | |
OFL-W3: A One-Shot Federated Learning System on Web 3.0 | VLDB | 2024 | [حانة] | |
Contributions Estimation in Federated Learning: A Comprehensive Experimental Evaluation | VLDB | 2024 | [حانة] | |
OFL-W3: A One-shot Federated Learning System on Web 3.0 | VLDB | 2024 | [حانة] | |
Uldp-FL: Federated Learning with Across Silo User-Level Differential Privacy. | VLDB | 2024 | [حانة] | |
FedSM: A Practical Federated Shared Mobility System. | VLDB | 2024 | [حانة] | |
FedSQ: A Secure System for Federated Vector Similarity Queries | VLDB | 2024 | [حانة] | |
Performance-Based Pricing of Federated Learning via Auction | مجموعة علي بابا | 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 | [حانة] |
Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs | جامعة كولومبيا | ICDE | 2023 | [PUB] [CODE] |
FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge | قليل | ICDE | 2023 | [PUB] [PDF] |
Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices | SJTU | ICDE | 2023 | [PUB] [PDF] |
Federated IoT Interaction Vulnerability Analysis | جامعة ولاية ميشيغان | ICDE | 2023 | [حانة] |
Distribution-Regularized Federated Learning on Non-IID Data | BUAA | ICDE | 2023 | [حانة] |
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 | [حانة] |
FedGTA: Topology-aware Averaging for Federated Graph Learning. | قليل | VLDB | 2023 | [PUB] [CODE] |
FS-Real: A Real-World Cross-Device Federated Learning Platform. | مجموعة علي بابا | VLDB | 2023 | [PUB] [PDF] [CODE] |
Federated Calibration and Evaluation of Binary Classifiers. | ميتا | VLDB | 2023 | [PUB] [PDF] [CODE] |
Olive: Oblivious Federated Learning on Trusted Execution Environment Against the Risk of Sparsification. | Kyoto University | VLDB | 2023 | [PUB] [PDF] [CODE] |
Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System. | جامعة سنغافورة الوطنية | VLDB | 2023 | [PUB] [CODE] |
Differentially Private Vertical Federated Clustering. | Purdue University | VLDB | 2023 | [PUB] [PDF] [CODE] |
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity. | علي بابا | VLDB | 2023 | [PUB] [PDF] [CODE] |
Secure Shapley Value for Cross-Silo Federated Learning. | Kyoto University | 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. | جامعة سنغافورة الوطنية | 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. | يضرب | 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 | [حانة] |
Federated Learning on Non-IID Data Silos: An Experimental Study. | جامعة سنغافورة الوطنية | ICDE | 2022 | [PUB] [PDF] [CODE] |
Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing | USTC | ICDE | 2022 | [حانة] |
Samba: A System for Secure Federated Multi-Armed Bandits | جامعة. كليرمون أوفيرني | 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 | [حانة] |
Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning | USTC | ICDE | 2022 | [حانة] |
An Introduction to Federated Computation | University of Warwick; فيسبوك | SIGMOD Tutorial | 2022 | [حانة] |
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data | PKU; تينسنت | 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 | جامعة سنغافورة الوطنية | ICDE | 2021 | [PUB] [PDF] [CODE] |
Efficient Federated-Learning Model Debugging | USTC | ICDE | 2021 | [حانة] |
Federated Matrix Factorization with Privacy Guarantee | بوردو | VLDB | 2021 | [حانة] |
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 | [حانة] |
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 | [حانة] |
ExDRa: Exploratory Data Science on Federated Raw Data | سيمنز | SIGMOD | 2021 | [حانة] |
Joint blockchain and federated learning-based offloading in harsh edge computing environments | TJU | SIGMOD workshop | 2021 | [حانة] |
Privacy Preserving Vertical Federated Learning for Tree-based Models | جامعة سنغافورة الوطنية | 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).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning | INFOCOM | 2024 | [حانة] | |
Strategic Data Revocation in Federated Unlearning | INFOCOM | 2024 | [حانة] | |
FedTC: Enabling Communication-Efficient Federated Learning via Transform Coding | INFOCOM | 2024 | [حانة] | |
Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization | INFOCOM | 2024 | [حانة] | |
FairFed: Improving Fairness and Efficiency of Contribution Evaluation in Federated Learning via Cooperative Shapley Value | INFOCOM | 2024 | [حانة] | |
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service | INFOCOM | 2024 | [حانة] | |
Tomtit: Hierarchical Federated Fine-Tuning of Giant Models based on Autonomous Synchronization | INFOCOM | 2024 | [حانة] | |
BR-DeFedRL: Byzantine-Robust Decentralized Federated Reinforcement Learning with Fast Convergence and Communication Efficiency | INFOCOM | 2024 | [حانة] | |
Titanic: Towards Production Federated Learning with Large Language Models | INFOCOM | 2024 | [حانة] | |
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression | INFOCOM | 2024 | [حانة] | |
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes | INFOCOM | 2024 | [حانة] | |
Federated Analytics-Empowered Frequent Pattern Mining for Decentralized Web 3.0 Applications | INFOCOM | 2024 | [حانة] | |
GraphProxy: Communication-Efficient Federated Graph Learning with Adaptive Proxy | INFOCOM | 2024 | [حانة] | |
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 | [حانة] | |
Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning | INFOCOM | 2024 | [حانة] | |
Heroes: Lightweight Federated Learning with Neural Composition and Adaptive Local Update in Heterogeneous Edge Networks | INFOCOM | 2024 | [حانة] | |
Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiency | INFOCOM | 2024 | [حانة] | |
Federated Offline Policy Optimization with Dual Regularization | INFOCOM | 2024 | [حانة] | |
A Semi-Asynchronous Decentralized Federated Learning Framework via Tree-Graph Blockchain | INFOCOM | 2024 | [حانة] | |
SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation | INFOCOM | 2024 | [حانة] | |
Towards Efficient Asynchronous Federated Learning in Heterogeneous Edge Environments | INFOCOM | 2024 | [حانة] | |
Federated Learning Based Integrated Sensing, Communications, and Powering Over 6G Massive-MIMO Mobile Networks | INFOCOM workshop | 2024 | [حانة] | |
Decentralized Federated Learning Under Free-riders: Credibility Analysis | INFOCOM workshop | 2024 | [حانة] | |
TrustBandit: Optimizing Client Selection for Robust Federated Learning Against Poisoning Attacks | INFOCOM workshop | 2024 | [حانة] | |
Cascade: Enhancing Reinforcement Learning with Curriculum Federated Learning and Interference Avoidance — A Case Study in Adaptive Bitrate Selection | INFOCOM workshop | 2024 | [حانة] | |
Efficient Adapting for Vision-language Foundation Model in Edge Computing Based on Personalized and Multi-Granularity Federated Learning | INFOCOM workshop | 2024 | [حانة] | |
Distributed Link Heterogeneity Exploitation for Attention-Weighted Robust Federated Learning in 6G Networks | INFOCOM workshop | 2024 | [حانة] | |
GAN-Based Privacy Abuse Attack on Federated Learning in IoT Networks | INFOCOM workshop | 2024 | [حانة] | |
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 | [حانة] | |
Unbiased Federated Learning for Heterogeneous Data Under Unreliable Links | INFOCOM workshop | 2024 | [حانة] | |
Efficient Client Sampling with Compression in Heterogeneous Federated Learning | INFOCOM workshop | 2024 | [حانة] | |
Reputation-Aware Scheduling for Secure Internet of Drones: A Federated Multi-Agent Deep Reinforcement Learning Approach | INFOCOM workshop | 2024 | [حانة] | |
Two-Timescale Energy Optimization for Wireless Federated Learning | INFOCOM workshop | 2024 | [حانة] | |
A Data Reconstruction Attack Against Vertical Federated Learning Based on Knowledge Transfer | INFOCOM workshop | 2024 | [حانة] | |
Federated Learning for Energy-efficient Cooperative Perception in Connected and Autonomous Vehicles | INFOCOM workshop | 2024 | [حانة] | |
Federated Learning-Based Cooperative Model Training for Task-Oriented Semantic Communication | INFOCOM workshop | 2024 | [حانة] | |
FedBF16-Dynamic: Communication-Efficient Federated Learning with Adaptive Transmission | INFOCOM workshop | 2024 | [حانة] | |
Designing Robust 6G Networks with Bimodal Distribution for Decentralized Federated Learning | INFOCOM workshop | 2024 | [حانة] | |
Federated Distributed Deep Reinforcement Learning for Recommendation-enabled Edge Caching | INFOCOM workshop | 2024 | [حانة] | |
Joint Optimization of Charging Time and Resource Allocation in Wireless Power Transfer Assisted Federated Learning | INFOCOM workshop | 2024 | [حانة] | |
Joint Client Selection and Privacy Compensation for Differentially Private Federated Learning | INFOCOM workshop | 2024 | [حانة] | |
Wireless Hierarchical Federated Aggregation Weights Design with Loss-Based-Heterogeneity | INFOCOM workshop | 2024 | [حانة] | |
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 | [حانة] |
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 | [حانة] |
Co-clustering for Federated Recommender System | UIUC | WWW | 2024 | [حانة] |
Incentive and Dynamic Client Selection for Federated Unlearning | BUPT | WWW | 2024 | [حانة] |
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 | [حانة] |
Cardinality Counting in "Alcatraz": A Privacy-aware Federated Learning Approach | CSIRO's Data61 | WWW | 2024 | [حانة] |
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 | [حانة] |
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 | [حانة] |
Exploring Representational Similarity Analysis to Protect Federated Learning from Data Poisoning | SYSU | WWW (Companion Volume) | 2024 | [حانة] |
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 | [حانة] |
HBIAS FedAvg: Smooth Federated Learning Transition for In-use Edge Models | IIT | WWW (Companion Volume) | 2024 | [حانة] |
Phoenix: A Federated Generative Diffusion Model | UW | WWW (Companion Volume) | 2024 | [حانة] |
Federated Learning in Large Model Era: Vision-Language Model for Smart City Safety Operation Management | ENN; UPC | WWW (Companion Volume) | 2024 | [حانة] |
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 | [حانة] |
Detecting Poisoning Attacks on Federated Learning Using Gradient-Weighted Class Activation Mapping | ISEP | WWW (Companion Volume) | 2024 | [حانة] |
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 | [حانة] |
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 | جامعة العلوم والتكنولوجيا | 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 | [حانة] |
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 | [حانة] |
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 | [حانة] |
Privacy-Preserving Online Content Moderation: A Federated Learning Use Case. | يقطع | WWW (Companion Volume) | 2023 | [PUB] [PDF] |
Privacy-Preserving Online Content Moderation with Federated Learning. | يقطع | WWW (Companion Volume) | 2023 | [حانة] |
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 | [حانة] |
1st Workshop on Federated Learning Technologies1st Workshop on Federated Learning Technologies | University of Turin | WWW (Companion Volume) | 2023 | [حانة] |
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 | الخميس | INFOCOM | 2023 | [حانة] |
A Reinforcement Learning Approach for Minimizing Job Completion Time in Clustered Federated Learning | Southeast University | INFOCOM | 2023 | [حانة] |
Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning | USTC | INFOCOM | 2023 | [PUB] [PDF] |
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices | جامعة قوانغدونغ للتكنولوجيا | INFOCOM | 2023 | [PUB] [PDF] |
AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation | HUST | INFOCOM | 2023 | [حانة] |
Asynchronous Federated Unlearning | University of Toronto | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization | جامعة الأمير سلطان | INFOCOM | 2023 | [PUB] [PDF] |
Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing | Beihang University | INFOCOM | 2023 | [حانة] |
Federated Learning under Heterogeneous and Correlated Client Availability | Inria | INFOCOM | 2023 | [PUB] [PDF] [CODE] |
Federated Learning with Flexible Control | آي بي إم | 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 | [حانة] |
Joint Participation Incentive and Network Pricing Design for Federated Learning | جامعة نورث وسترن | INFOCOM | 2023 | [حانة] |
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 | [حانة] |
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 | [حانة] |
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 | جامعة أوبورن | INFOCOM | 2023 | [PUB] [PDF] |
TVFL: Tunable Vertical Federated Learning towards Communication-Efficient Model Serving | USTC | INFOCOM | 2023 | [حانة] |
PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning | جامعة ولاية ميشيغان | MobiCom | 2022 | [PUB] [PDF] [CODE] |
NestFL: efficient federated learning through progressive model pruning in heterogeneous edge computing | pmlabs | MobiCom(Poster) | 2022 | [حانة] |
Federated learning-based air quality prediction for smart cities using BGRU model | IITM | MobiCom(Poster) | 2022 | [حانة] |
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 | [حانة] |
Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical Gradient Blending | University of Toronto | INFOCOM | 2022 | [حانة] |
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 | NEU | INFOCOM | 2022 | [PUB] [CODE] |
A Profit-Maximizing Model Marketplace with Differentially Private Federated Learning | CUHK; AIRS | INFOCOM | 2022 | [حانة] |
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 | Yonsei University | WWW | 2022 | [حانة] |
Federated Unlearning via Class-Discriminative Pruning | PolyU | WWW | 2022 | [PUB] [PDF] [CODE] |
FedKC: Federated Knowledge Composition for Multilingual Natural Language Understanding | بوردو | WWW | 2022 | [حانة] |
Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing. | WWW (Companion Volume) | 2022 | ||
Federated Bandit: A Gossiping Approach | University of California | SIGMETRICS | 2021 | [PUB] [PDF] |
Hermes: an efficient federated learning framework for heterogeneous mobile clients | Duke University | MobiCom | 2021 | [حانة] |
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 | [حانة] |
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation. | بوردو | INFOCOM | 2021 | [PUB] [PDF] |
FAIR: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation | الخميس | INFOCOM | 2021 | [حانة] |
Sample-level Data Selection for Federated Learning | USTC | INFOCOM | 2021 | [حانة] |
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices | Xidian University; CAS | INFOCOM | 2021 | [PUB] [PDF] |
Cost-Effective Federated Learning Design | CUHK; AIRS; Yale University | INFOCOM | 2021 | [PUB] [PDF] |
An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective | The UBC | INFOCOM | 2021 | [حانة] |
Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing | USTC | INFOCOM | 2021 | [حانة] |
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 | [حانة] |
P-FedAvg: Parallelizing Federated Learning with Theoretical Guarantees | SYSU; Guangdong Key Laboratory of Big Data Analysis and Processing | INFOCOM | 2021 | [حانة] |
Meta-HAR: Federated Representation Learning for Human Activity Recognition. | جامعة ألبرتا | 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 | إيموري | WWW | 2021 | [PUB] [CODE] |
Hierarchical Personalized Federated Learning for User Modeling | USTC | WWW | 2021 | [حانة] |
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 | [حانة] |
Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled Wireless Networks. | Nanjing University | INFOCOM | 2020 | [حانة] |
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 | الخميس | INFOCOM | 2020 | [PUB] [CODE] |
Billion-scale federated learning on mobile clients: a submodel design with tunable privacy | SJTU | MobiCom | 2020 | [حانة] |
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 | [حانة] |
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).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems. | لجنة المساعدة الإنمائية | 2024 | [حانة] | |
Fake Node-Based Perception Poisoning Attacks against Federated Object Detection Learning in Mobile Computing Networks | لجنة المساعدة الإنمائية | 2024 | [حانة] | |
Flagger: Cooperative Acceleration for Large-Scale Cross-Silo Federated Learning Aggregation | ISCA | 2024 | [حانة] | |
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 | [حانة] |
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 | [حانة] |
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 | [حانة] | |
ALS Algorithm for Robust and Communication-Efficient Federated Learning | EuroSys workshop | 2024 | [حانة] | |
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission. | EuroSys workshop | 2024 | [حانة] | |
Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting. | TPDS | 2024 | [حانة] | |
SR-FDIL: Synergistic Replay for Federated Domain-Incremental Learning | TPDS | 2024 | [حانة] | |
FedVeca: Federated Vectorized Averaging on Non-IID Data With Adaptive Bi-Directional Global Objective | TPDS | 2024 | [حانة] | |
Trusted Model Aggregation With Zero-Knowledge Proofs in Federated Learning. | TPDS | 2024 | [حانة] | |
Accelerating Communication-Efficient Federated Multi-Task Learning With Personalization and Fairness. | TPDS | 2024 | [حانة] | |
Privacy-Preserving Data Selection for Horizontal and Vertical Federated Learning. | TPDS | 2024 | [حانة] | |
High-Performance Hardware Acceleration Architecture for Cross-Silo Federated Learning | TPDS | 2024 | [حانة] | |
Joint Participant and Learning Topology Selection for Federated Learning in Edge Clouds | TPDS | 2024 | [حانة] | |
Synchronize Only the Immature Parameters: Communication-Efficient Federated Learning By Freezing Parameters Adaptively | SJTU | TPDS | 2024 | [حانة] |
FedREM: Guided Federated Learning in the Presence of Dynamic Device Unpredictability | SYSU | TPDS | 2024 | [حانة] |
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 | [حانة] |
FedICT: Federated Multi-Task Distillation for Multi-Access Edge Computing | يوكاس | TPDS | 2024 | [PUB] [PDF] |
Collaboration in Federated Learning With Differential Privacy: A Stackelberg Game Analysis | SYSU | TPDS | 2024 | [حانة] |
FAST: Enhancing Federated Learning Through Adaptive Data Sampling and Local Training | USTC | TPDS | 2024 | [حانة] |
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 | الخميس | TPDS | 2024 | [PUB] [PDF] |
FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios. | TCAD | 2024 | [حانة] | |
Personalized Meta-Federated Learning for IoT-Enabled Health Monitoring | TCAD | 2024 | [حانة] | |
NebulaFL: Self-Organizing Efficient Multilayer Federated Learning Framework With Adaptive Load Tuning in Heterogeneous Edge Systems | TCAD | 2024 | [حانة] | |
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance | TCAD | 2024 | [حانة] | |
FedStar: Efficient Federated Learning on Heterogeneous Communication Networks | USTC | TCAD | 2024 | [حانة] |
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 | يضرب | TCAD | 2024 | [حانة] |
BSR-FL: An Efficient Byzantine-Robust Privacy-Preserving Federated Learning Framework | ح | 2024 | [حانة] | |
User-Distribution-Aware Federated Learning for Efficient Communication and Fast Inference | ECNU; شو | ح | 2024 | [حانة] |
FedRFQ: Prototype-Based Federated Learning With Reduced Redundancy, Minimal Failure, and Enhanced Quality | SDU | ح | 2024 | [PUB] [PDF] |
Value of Information: A Comprehensive Metric for Client Selection in Federated Edge Learning | SDU | ح | 2024 | [حانة] |
Age-Aware Data Selection and Aggregator Placement for Timely Federated Continual Learning in Mobile Edge Computing | DLUT | ح | 2024 | [حانة] |
FedGKD: Toward Heterogeneous Federated Learning via Global Knowledge Distillation | HUST | ح | 2024 | [PUB] [PDF] |
Digital Twin-Assisted Federated Learning Service Provisioning Over Mobile Edge Networks | SDU | ح | 2024 | [حانة] |
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 | [حانة] | |
Towards Practical Few-shot Federated NLP | EuroSys workshop | 2023 | [حانة] | |
Can Fair Federated Learning Reduce the need for Personalisation? | EuroSys workshop | 2023 | [حانة] | |
Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates | EuroSys workshop | 2023 | [حانة] | |
Towards Robust and Bias-free Federated Learning | EuroSys workshop | 2023 | [حانة] | |
FedTree: A Federated Learning System For Trees | UC Berkeley | MLSys | 2023 | [PUB] [CODE] |
FLINT: A Platform for Federated Learning Integration | ينكدين | 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 | [حانة] |
Lightweight Blockchain-Empowered Secure and Efficient Federated Edge Learning | University of Exeter | ح | 2023 | [حانة] |
Towards Data-Independent Knowledge Transfer in Model-Heterogeneous Federated Learning | PolyU | ح | 2023 | [حانة] |
A New Federated Scheduling Algorithm for Arbitrary-Deadline DAG Tasks | NEFU | ح | 2023 | [حانة] |
Privacy-Enhanced Decentralized Federated Learning at Dynamic Edge | SDU | ح | 2023 | [حانة] |
Byzantine-Resilient Federated Learning at Edge | SDU | ح | 2023 | [PUB] [PDF] |
PrivAim: A Dual-Privacy Preserving and Quality-Aware Incentive Mechanism for Federated Learning | CSU | ح | 2023 | [حانة] |
Accelerating Federated Learning With a Global Biased Optimiser | University of Exeter | ح | 2023 | [PUB] [PDF] [CODE] |
Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore Platforms | TU Dortmund University | ح | 2023 | [PUB] [CODE] |
Sandbox Computing: A Data Privacy Trusted Sharing Paradigm Via Blockchain and Federated Learning. | BUPT | ح | 2023 | [حانة] |
CHEESE: Distributed Clustering-Based Hybrid Federated Split Learning Over Edge Networks | SUDA | TPDS | 2023 | [حانة] |
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 | [حانة] |
Energy-Aware, Device-to-Device Assisted Federated Learning in Edge Computing | ANU | TPDS | 2023 | [حانة] |
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 | [حانة] |
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 | [حانة] |
On Model Transmission Strategies in Federated Learning With Lossy Communications | SZU | TPDS | 2023 | [حانة] |
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 | يضرب | 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. | يضرب | TPDS | 2023 | [حانة] |
Multi-Job Intelligent Scheduling With Cross-Device Federated Learning. | بايدو | TPDS | 2023 | [PUB] [PDF] |
Data-Centric Client Selection for Federated Learning Over Distributed Edge Networks. | IIT | TPDS | 2023 | [حانة] |
GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication. | HKBU | TPDS | 2023 | [حانة] |
FedMDS: An Efficient Model Discrepancy-Aware Semi-Asynchronous Clustered Federated Learning Framework. | CQU | TPDS | 2023 | [حانة] |
HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated Learning in Mobile Edge Computing. | DUT | TPDS | 2023 | [حانة] |
Data selection for efficient model update in federated learning | EuroSys workshop | 2022 | [حانة] | |
Empirical analysis of federated learning in heterogeneous environments | EuroSys workshop | 2022 | [حانة] | |
BAFL: A Blockchain-Based Asynchronous Federated Learning Framework | ح | 2022 | [PUB] [CODE] | |
L4L: Experience-Driven Computational Resource Control in Federated Learning | ح | 2022 | [حانة] | |
Adaptive Federated Learning on Non-IID Data With Resource Constraint | ح | 2022 | [حانة] | |
Locking Protocols for Parallel Real-Time Tasks With Semaphores Under Federated Scheduling. | TCAD | 2022 | [حانة] | |
Client Scheduling and Resource Management for Efficient Training in Heterogeneous IoT-Edge Federated Learning | ECNU | TCAD | 2022 | [حانة] |
PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT Systems. | ECNU | TCAD | 2022 | [حانة] |
FHDnn: communication efficient and robust federated learning for AIoT networks | جامعة كاليفورنيا في سان دييغو | لجنة المساعدة الإنمائية | 2022 | [حانة] |
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 | الخميس | TPDS | 2022 | [حانة] |
$f$funcX: Federated Function as a Service for Science. | جامعة العلوم والتكنولوجيا | 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 | [حانة] |
TDFL: Truth Discovery Based Byzantine Robust Federated Learning | قليل | TPDS | 2022 | [حانة] |
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. | الخميس | TPDS | 2022 | [حانة] |
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 | [حانة] |
LightFed: An Efficient and Secure Federated Edge Learning System on Model Splitting. | CSU | TPDS | 2022 | [حانة] |
On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning. | بوردو | TPDS | 2022 | [PUB] [PDF] [CODE] |
Incentive-Aware Autonomous Client Participation in Federated Learning. | جامعة صن يات صن | TPDS | 2022 | [حانة] |
Communicational and Computational Efficient Federated Domain Adaptation. | HKUST | TPDS | 2022 | [حانة] |
Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning. | NTU | TPDS | 2022 | [حانة] |
Differentially Private Byzantine-Robust Federated Learning. | Qufu Normal University | TPDS | 2022 | [حانة] |
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 | [حانة] |
Differentially Private Federated Temporal Difference Learning. | Stony Brook University | TPDS | 2022 | [حانة] |
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. | ميتا الذكاء الاصطناعي | MLSys | 2022 | [PUB] [PDF] |
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning | جامعة جنوب كاليفورنيا | MLSys | 2022 | [PUB] [PDF] [CODE] |
Accelerated Training via Device Similarity in Federated Learning | EuroSys workshop | 2021 | [حانة] | |
Towards Federated Learning with Attention Transfer to Mitigate System and Data Heterogeneity of Clients | EuroSys workshop | 2021 | [حانة] | |
Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization | EuroSys workshop | 2021 | [حانة] | |
SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead | University of Warwick | ح | 2021 | [PDF] [PUB] [CODE] |
Efficient Federated Learning for Cloud-Based AIoT Applications | ECNU | TCAD | 2021 | [حانة] |
HADFL: Heterogeneity-aware Decentralized Federated Learning Framework | USTC | لجنة المساعدة الإنمائية | 2021 | [PDF] [PUB] |
Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration. | GMU | لجنة المساعدة الإنمائية | 2021 | [PDF] [PUB] |
FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control. | ECNU | لجنة المساعدة الإنمائية | 2021 | [حانة] |
Oort: Efficient Federated Learning via Guided Participant Selection | جامعة ميشيغان | 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 | ذنيب ذنب قصير | 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 | [حانة] |
Mutual Information Driven Federated Learning. | Deakin University | TPDS | 2021 | [حانة] |
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 | [حانة] |
FedScale: Benchmarking Model and System Performance of Federated Learning | جامعة ميشيغان | SOSP workshop / ICML 2022 | 2021 | [PUB] [PDF] [CODE] [解读] |
Redundancy in cost functions for Byzantine fault-tolerant federated learning | SOSP workshop | 2021 | [حانة] | |
Towards an Efficient System for Differentially-private, Cross-device Federated Learning | SOSP workshop | 2021 | [حانة] | |
GradSec: a TEE-based Scheme Against Federated Learning Inference Attacks | SOSP workshop | 2021 | [حانة] | |
Community-Structured Decentralized Learning for Resilient EI. | SOSP workshop | 2021 | [حانة] | |
Separation of Powers in Federated Learning (Poster Paper) | IBM Research | SOSP workshop | 2021 | [PUB] [PDF] |
Towards federated unsupervised representation learning | EuroSys workshop | 2020 | [حانة] | |
CoLearn: enabling federated learning in MUD-compliant IoT edge networks | EuroSys workshop | 2020 | [حانة] | |
LDP-Fed: federated learning with local differential privacy. | EuroSys workshop | 2020 | [حانة] | |
Accelerating Federated Learning via Momentum Gradient Descent. | USTC | TPDS | 2020 | [PUB] [PDF] |
Towards Fair and Privacy-Preserving Federated Deep Models. | جامعة سنغافورة الوطنية | TPDS | 2020 | [PUB] [PDF] [CODE] |
Federated Optimization in Heterogeneous Networks | CMU | MLSys | 2020 | [PUB] [PDF] [CODE] |
Towards Federated Learning at Scale: System Design | جوجل | 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).
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
F-CodeLLM: A Federated Learning Framework for Adapting Large Language Models to Practical Software Development | SYSU | ICSE Companion | 2024 | حانة |
Raft Protocol for Fault Tolerance and Self-Recovery in Federated Learning | SINTEF Digital | SEAMS@ICSE | 2024 | حانة |
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 | حانة | |
Federated Machine Learning as a Self-Adaptive Problem | SEAMS@ICSE workshop | 2021 | حانة |
This section partially refers to DBLP search engine and repositories Awesome-Federated-Learning-on-Graph-and-GNN-papers and Awesome-Federated-Machine-Learning.
عنوان | انتساب | مكان | سنة | مواد |
---|---|---|---|---|
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. Graph. ؟ | 2023 | [PUB] [PDF] |
Personalized Subgraph Federated Learning | KAIST | ICML ? | 2023 | [PDF] |
Semi-decentralized Federated Ego Graph Learning for Recommendation | جامعة العلوم والتكنولوجيا | 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 | [حانة] |
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. Graph. ؟ | 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 | [حانة] |
GraphCS: Graph-based client selection for heterogeneity in federated learning | NUDT | J. Parallel Distributed Comput. | 2023 | [حانة] |
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 | [حانة] |
Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection. | HVL | IEEE J. Biomed. Health Informatics | 2023 | [حانة] |
Federated Learning-Based Cross-Enterprise Recommendation With Graph Neural | IEEE Trans. Ind. Informatics | 2023 | [حانة] | |
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning | ZJUT | IEEE Trans. Comput. شركة نفط الجنوب. Syst. | 2023 | [PUB] [PDF] [CODE] |
ESA-FedGNN: Efficient secure aggregation for federated graph neural networks. | Peer Peer Netw. تطبيق. | 2023 | [حانة] | |
FedCKE: Cross-Domain Knowledge Graph Embedding in Federated Learning | SWJTU | IEEE Trans. البيانات الضخمة | 2023 | [حانة] |
Asynchronous federated learning with directed acyclic graph-based blockchain in edge computing: Overview, design, and challenges. | Expert Syst. تطبيق. | 2023 | [حانة] | |
FedGR: Federated Graph Neural Network for Recommendation System | CUPT | البديهيات | 2023 | [حانة] |
S-Glint: Secure Federated Graph Learning With Traffic Throttling and Flow Scheduling. | IEEE Trans. Green Commun. Netw. | 2023 | [حانة] | |
FedAGCN: A traffic flow prediction framework based on federated learning and Asynchronous Graph Convolutional Network | تطبيق. Soft Comput. | 2023 | [حانة] | |
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 | [حانة] |
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 | علي بابا | 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 | جامعة جنوب كاليفورنيا | 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 | التعلم الموحد | 2022 | [حانة] |
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 | [حانة] |
Federated Spatio-Temporal Traffic Flow Prediction Based on Graph Convolutional Network | TJU | WCSP | 2022 | [حانة] |
A federated graph neural network framework for privacy-preserving personalization | الخميس | اتصالات الطبيعة | 2022 | [PUB] [CODE] [解读] |
Malicious Transaction Identification in Digital Currency via Federated Graph Deep Learning | قليل | INFOCOM Workshops | 2022 | [حانة] |
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 | جامعة رايس | ICASSP | 2022 | [PUB] [PDF] [CODE] |
Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization | جامعة كاليفورنيا | ICASSP | 2022 | [PUB] [PDF] [CODE] |
Graph-regularized federated learning with shareable side information | NWPU | Knowl. Based Syst. | 2022 | [حانة] |
Federated knowledge graph completion via embedding-contrastive learning kg. | ZJU | Knowl. Based Syst. | 2022 | [حانة] |
Federated Graph Learning with Periodic Neighbour Sampling | HKU | IWQoS | 2022 | [حانة] |
FedGSL: Federated Graph Structure Learning for Local Subgraph Augmentation. | البيانات الضخمة | 2022 | [حانة] | |
Domain-Aware Federated Social Bot Detection with Multi-Relational Graph Neural Networks. | UCAS; CAS | IJCNN | 2022 | [حانة] |
A Federated Multi-Server Knowledge Graph Embedding Framework For Link Prediction. | ICTAI | 2022 | [حانة] | |
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 | [حانة] |
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 | كثافة العمليات. J. Bio Inspired |