Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments
This work aims to improve the efficiency and performance of federated learning for practitioners dealing with non-IID data distributions.
This paper addresses performance and network communication challenges in federated learning, particularly in non-IID data environments. It introduces a federated multi-mini-batch approach that achieves comparable performance to centralized training and outperforms federated averaging in non-IID settings, while also improving communication efficiency.
Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) across the clients. To address the former challenge, we introduce the federated-centralized concordance property and show that the federated single-mini-batch training approach can achieve comparable performance as the corresponding centralized training in the Non-IID environments. To deal with the latter, we present the federated multi-mini-batch approach and illustrate that it can establish a trade-off between the performance and communication efficiency and outperforms federated averaging in the Non-IID settings.