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Conference and Workshop Papers
- 2024
[c10]Julien Fageot, Sadegh Farhadkhani, Lê-Nguyên Hoang, Oscar Villemaud:
Generalized Bradley-Terry Models for Score Estimation from Paired Comparisons. AAAI 2024: 20379-20386- [c9]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych:
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates. ICML 2024 - [c8]Sadegh Farhadkhani
, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
Brief Announcement: A Case for Byzantine Machine Learning. PODC 2024: 131-134 - 2023
- [c7]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. AISTATS 2023: 1232-1300 - [c6]El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang:
On the Strategyproofness of the Geometric Median. AISTATS 2023: 2603-2640 - [c5]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan:
Robust Collaborative Learning with Linear Gradient Overhead. ICML 2023: 9761-9813 - [c4]Martijn de Vos, Sadegh Farhadkhani, Rachid Guerraoui, Anne-Marie Kermarrec, Rafael Pires, Rishi Sharma:
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication. NeurIPS 2023 - 2022
- [c3]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums. ICML 2022: 6246-6283 - [c2]Sadegh Farhadkhani, Rachid Guerraoui, Lê Nguyên Hoang, Oscar Villemaud:
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks. ICML 2022: 6284-6323 - 2021
- [c1]El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Arsany Guirguis, Lê-Nguyên Hoang, Sébastien Rouault:
Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning). NeurIPS 2021: 25044-25057
Informal and Other Publications
- 2024
- [i11]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, Geovani Rizk, Sasha Voitovych:
Tackling Byzantine Clients in Federated Learning. CoRR abs/2402.12780 (2024) - [i10]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot:
On the Relevance of Byzantine Robust Optimization Against Data Poisoning. CoRR abs/2405.00491 (2024) - 2023
- [i9]Youssef Allouah, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity. CoRR abs/2302.01772 (2023) - [i8]Martijn de Vos, Sadegh Farhadkhani, Rachid Guerraoui, Anne-Marie Kermarrec, Rafael Pires, Rishi Sharma:
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication. CoRR abs/2310.01972 (2023) - 2022
- [i7]Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang, Oscar Villemaud:
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks. CoRR abs/2202.08578 (2022) - [i6]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Rafael Pinot, John Stephan:
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums. CoRR abs/2205.12173 (2022) - [i5]Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê Nguyên Hoang, Rafael Pinot, John Stephan:
Making Byzantine Decentralized Learning Efficient. CoRR abs/2209.10931 (2022) - [i4]El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Nirupam Gupta, Lê-Nguyên Hoang, Rafael Pinot, John Stephan:
On the Impossible Safety of Large AI Models. CoRR abs/2209.15259 (2022) - 2021
- [i3]El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang:
On the Strategyproofness of the Geometric Median. CoRR abs/2106.02394 (2021) - [i2]Sadegh Farhadkhani, Rachid Guerraoui, Lê-Nguyên Hoang:
Strategyproof Learning: Building Trustworthy User-Generated Datasets. CoRR abs/2106.02398 (2021) - 2020
- [i1]El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui, Arsany Guirguis, Lê Nguyên Hoang, Sébastien Rouault:
Collaborative Learning as an Agreement Problem. CoRR abs/2008.00742 (2020)
Coauthor Index
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