  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.
