AI RESEARCH
Provable Acceleration of Distributed Optimization with Local Updates
arXiv CS.LG
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ArXi:2601.03442v3 Announce Type: replace-cross In conventional distributed optimization, each agent performs a single local update between two communication rounds with its neighbors to synchronize solutions. Inspired by the success of using multiple local updates in federated learning, incorporating local updates into distributed optimization has recently attracted increasing attention.