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

arXiv: https://arxiv.org/abs/2011.07006 · HTML: https://mlsanity.com/paper/2011.07006/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation](https://mlsanity.com/paper/2205.11096/) (2022)
- [Anomaly Detection via Federated Learning](https://mlsanity.com/paper/2210.06614/) (2022)
- [Optimisation of federated learning settings under statistical heterogeneity variations](https://mlsanity.com/paper/2406.06340/) (2024)

## Abstract (arXiv)

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.