AI RESEARCH
Distributed Perceptron under Bounded Staleness, Partial Participation, and Noisy Communication
arXiv CS.LG
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ArXi:2601.10705v3 Announce Type: replace We study a semi-asynchronous client-server perceptron trained via iterative parameter mixing (IPM-style averaging): clients run local perceptron updates and a server forms a global model by aggregating the updates that arrive in each communication round.