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Federated learning has faced performance and network communicationchallenges, especially in the environments where the data is not independentand identically distributed (IID) across the clients. To address the formerchallenge, we introduce the federated-centralized concordance property and showthat the federated single-mini-batch training approach can achieve comparableperformance as the corresponding centralized training in the Non-IIDenvironments. To deal with the latter, we present the federatedmulti-mini-batch approach and illustrate that it can establish a trade-offbetween the performance and communication efficiency and outperforms federatedaveraging in the Non-IID settings.

Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments