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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.
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@article{lee2023federated,
title = {Federated Learning in Temporal Heterogeneity},
author = {Junghwan Lee},
year = {2023},
eprint = {2309.09381},
archivePrefix = {arXiv},
url = {http://arxiv.org/abs/2309.09381v1}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/2309.09381)