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cs.LG2026
Accelerating Byzantine-Robust Distributed Learning with Compressed Communication via Double Momentum and Variance Reduction
Yanghao Li, Changxin Liu, Yuhao Yi
In collaborative and distributed learning, Byzantine robustness reflects a major facet of optimization algorithms. Such distributed algorithms are often accompanied by transmitting…
cs.LG2025
LiD-FL: Towards List-Decodable Federated Learning
Hong Liu, Liren Shan, Han Bao +3
Federated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This pap…
cs.LG2024
Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers
Yuhao Yi, Ronghui You, Hong Liu +3
Byzantine machine learning has garnered considerable attention in light of the unpredictable faults that can occur in large-scale distributed learning systems. The key to secure re…