arXiv Machine Learning
Sep 4

A Nesterov-Accelerated Byzantine-Robust Federated Learning

The paper proposes Byrd-NAFL, a Byzantine‑robust federated learning algorithm that incorporates Nesterov’s momentum and resilient aggregation rules. It achieves fast and safe convergence under non‑convex, smooth loss functions with relaxed gradient assumptions, and provides a finite‑time convergence guarantee. Experiments show that Byrd-NAFL outperforms existing methods in convergence speed, accuracy, and resilience to various malicious attacks.

By Lihan Xu, Xiaoyi Fan, Gang Wang, Runhao Zeng, Xiping Hu, Yanjie Dong
arXiv Machine Learning
Aug 6

Communication-Enhanced Tutoring for Efficient Decentralized Multi-Agent Reinforcement Learning

arXiv:2508. 13661v4 Announce Type: replace Abstract: Centralized Training with Decentralized Execution (CTDE) is the dominant paradigm in multi-agent reinforcement learning (MARL), enabling agents to act independently at test time while leveraging additional information during training.

By Maciej Wojtala, Bogusz Stefa\'nczyk, Dominik Bogucki, {\L}ukasz Lepak, Pawe{\l} Wawrzy\'nski