A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints
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, bringing together graph-based personalization, agnostic fairness, and compressed event-triggered ...
| Authors | Krishnendu S. Tharakan, Carlo Fischione |
|---|---|
| Primary category | cs.LG |
| Published | 27 August 2026 |
| Last updated at source | 29 August 2026 |
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