Decentralized Parameter-Free Online Learning with Compressed Gossip

ArXi:2605.27831v1 Announce Type: new 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