Unified analysis shows decentralized ProxSkip achieves linear speedup in number of nodes under stochastic gradients for non-convex problems.
Feder- ated learning with compression: Unified analysis and sharp guarantees,
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Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization
Unified analysis shows decentralized ProxSkip achieves linear speedup in number of nodes under stochastic gradients for non-convex problems.