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A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints

arXiv · AI, language, vision and robotics · article · Aug 27, 2026 · UTC

Decentralized learning systems aim to collaboratively train models across multiple clients without relying on a central coordinator. While decentralization improves scalability, privacy, and robustness, it also exacerbates three fundamental challenges: statistical heterogeneity across clients, fairness in client-level performance, and stringent communication constraints. This raises a natural question: \emph{how fair can decentralized learning be under limited communication?} We address this question by presenting a unified framework for decentralized learning under communication constraints,

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