Decentralized Parameter-Free Online Learning with Compressed Gossip
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Computer Science > Machine Learning
Title:Decentralized Parameter-Free Online Learning with Compressed Gossip
Abstract:We study decentralized online convex optimization when agents communicate over a graph and messages may be compressed. Classical decentralized online methods typically require learning-rate choices that depend on the horizon, comparator scale, or other problem parameters, while compressed communication introduces additional disagreement that must be controlled. We propose DECO-EF (DEcentralized COin-betting with Error Feedback), a decentralized parameter-free online learning algorithm that combines coin-betting predictions with compressed difference-based gossip. Each agent maintains a clean accumulated state and a compressed tracker, and communicates only compressed state differences during gossip steps. The method is parameter-free in the online-learning sense: it does not tune to the horizon, the comparator norm, or the learning rate. We prove expected comparator-adaptive network-regret bounds for DECO-EF under compressed communication. To the best of our knowledge, this gives the first expected sublinear network-regret guarantees for parameter-free decentralized online learning under compressed communication.
| Subjects: | Machine Learning (cs.LG); Signal Processing (eess.SP); Optimization and Control (math.OC) |
| MSC classes: | 68W10, 68W15, 68W40, 90C06, 90C35, 90C26 |
| ACM classes: | G.1.6; F.2.1; E.4 |
| Cite as: | arXiv:2605.27831 [cs.LG] |
| (or arXiv:2605.27831v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27831
arXiv-issued DOI via DataCite (pending registration)
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