FedSEA achieves O(sqrt(T)) regret for smooth convex losses and O(log T) for smooth strongly convex losses in federated online learning under stochastic adversary, with parallelization benefits when temporal heterogeneity is mild relative to gradient noise.
Tighter Regret Analysis and Optimization of Online Federated Learning , year=
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FedSEA: Achieving Benefit of Parallelization in Federated Online Learning
FedSEA achieves O(sqrt(T)) regret for smooth convex losses and O(log T) for smooth strongly convex losses in federated online learning under stochastic adversary, with parallelization benefits when temporal heterogeneity is mild relative to gradient noise.