RMFL replaces the standard exponentially weighted average of local gradients in federated momentum with reverse-decayed weights, and reports accuracy gains over MFL on three benchmarks under non-IID splits.
Communication-efficient adaptive federated learning,
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Rethinking the initialization of Momentum in Federated Learning with Heterogeneous Data
RMFL replaces the standard exponentially weighted average of local gradients in federated momentum with reverse-decayed weights, and reports accuracy gains over MFL on three benchmarks under non-IID splits.