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Federated Learning in Temporal Heterogeneity

09/17/2023

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by Junghwan Lee, et al.

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In this work, we explored federated learning in temporal heterogeneity across clients. We observed that global model obtained by 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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