Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity

3 February 2023

Papers citing "Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity"

9 / 9 papers shown

Title
Bayesian Robust Aggregation for Federated Learning Aleksandr Karakulev Usama Zafar Salman Toor Prashant Singh FedML 33 0 0 05 May 2025
Towards Trustworthy Federated Learning with Untrusted Participants Youssef Allouah R. Guerraoui John Stephan FedML 43 0 0 03 May 2025
Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection Jiahao Xu Zikai Zhang Rui Hu AAML FedML Presented at ResearchTrend Connect \| FedML on 28 Mar 2025 142 0 0 11 Mar 2025
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation Jiahao Xu Zikai Zhang Rui Hu 35 4 0 02 Sep 2024
On the Byzantine-Resilience of Distillation-Based Federated Learning Christophe Roux Max Zimmer S. Pokutta AAML 49 1 0 19 Feb 2024
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates Ahmad Rammal Kaja Gruntkowska Nikita Fedin Eduard A. Gorbunov Peter Richtárik 32 5 0 15 Oct 2023
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning Kerem Ozfatura Emre Ozfatura Alptekin Kupcu Deniz Gunduz AAML FedML 21 13 0 21 Aug 2022
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums Sadegh Farhadkhani R. Guerraoui Nirupam Gupta Rafael Pinot John Stephan FedML 29 67 0 24 May 2022
Approximate Byzantine Fault-Tolerance in Distributed Optimization Shuo Liu Nirupam Gupta Nitin H. Vaidya 28 42 0 22 Jan 2021