One-Shot Federated Learning for Model Clustering and Learning in Heterogeneous Environments. (arXiv:2209.10866v1 [cs.LG])

We propose a communication efficient approach for federated learning in
heterogeneous environments. The system heterogeneity is reflected in the
presence of $K$ different data distributions, with each user sampling data from
only one of $K$ distributions. The proposed approach requires only one
communication round between the users and server, thus significantly reducing
the communication cost. Moreover, the proposed method provides strong learning
guarantees in heterogeneous environments, by achieving the optimal mean-squared
error (MSE) rates in terms of the sample size, …

arxiv clustering environments federated learning