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cs.LG2026
When the Server Steps In: Calibrated Updates for Fair Federated Learning
Tianrun Yu, Kaixiang Zhao, Cheng Zhang +4
Federated learning (FL) has emerged as a transformative distributed learning paradigm, enabling multiple clients to collaboratively train a global model under the coordination of a…
cs.LG2025
Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
Yueyang Quan, Chang Wang, Shengjie Zhai +2
Decentralized min-max optimization allows multi-agent systems to collaboratively solve global min-max optimization problems by facilitating the exchange of model updates among neig…