Optimal regret bounds O(δ^{-1/2}√T) for convex and O(δ^{-1} log T) for strongly convex losses are achieved in distributed online convex optimization under compressed communication.
On the generalization ability of on-line learning algorithms.IEEE Transactions on Information Theory, 50(9):2050–2057,
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Distributed Online Convex Optimization with Compressed Communication: Optimal Regret and Applications
Optimal regret bounds O(δ^{-1/2}√T) for convex and O(δ^{-1} log T) for strongly convex losses are achieved in distributed online convex optimization under compressed communication.