Paper ID: 2309.09381 • Published Sep 17, 2023

Federated Learning in Temporal Heterogeneity

Junghwan Lee
TL;DR
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In this work, we explored federated learning in temporal heterogeneity across clients. We observed that global model obtained by \texttt{FedAvg} trained with fixed-length sequences shows faster convergence than varying-length sequences. We proposed methods to mitigate temporal heterogeneity for efficient federated learning based on the empirical observation.