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FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift

arXiv:2609.21057v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training without sharing raw data, but its performance degrades under non-IID data and stochastic client participation. Remedies built on classical Federated Averaging (FedAvg) typically presuppose t…

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  1. 2026-09-21 04:00 · arXiv cs.LG
    FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift

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