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

Reza Nasirigerdeh; Mohammad Bakhtiari; Reihaneh Torkzadehmahani; Amirhossein Bayat; Markus List; David B. Blumenthal; Jan Baumbach

arXiv · 2011.07006

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

Reza Nasirigerdeh·Mohammad Bakhtiari·Reihaneh Torkzadehmahani·Amirhossein Bayat·Markus List·David B. Blumenthal·Jan Baumbach

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Abstract

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.

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Reza Nasirigerdeh, Mohammad Bakhtiari, Reihaneh Torkzadehmahani, Amirhossein Bayat, Markus List, David B. Blumenthal, Jan Baumbach. 2020-11-13. Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments. https://arxiv.org/abs/2011.07006

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