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cs.LG2023★ 1 cited
Federated Learning Robust to Byzantine Attacks: Achieving Zero Optimality Gap
Shiyuan Zuo, Rongfei Fan, Han Hu +2
In this paper, we propose a robust aggregation method for federated learning (FL) that can effectively tackle malicious Byzantine attacks. At each user, model parameter is firstly…
cs.LG2023
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data
Zhuang Qi, Lei Meng, Zitan Chen +3
Federated Learning aims to learn a global model on the server side that generalizes to all clients in a privacy-preserving manner, by leveraging the local models from different cli…