AI RESEARCH
Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms
arXiv CS.LG
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ArXi:2604.20492v1 Announce Type: cross In this paper, it is shown, for the first time, that centralized performance is achievable in decentralized learning without sharing the local datasets. Specifically, when clients adopt an empirical risk minimization with relative-entropy regularization (ERM-RER) learning framework and a forward-backward communication between clients is established, it suffices to share the locally obtained Gibbs measures to achieve the same performance as that of a centralized ERM-RER with access to all the datasets.