# An Experimental Study of Class Imbalance in Federated Learning

arXiv: https://arxiv.org/abs/2109.04094 · HTML: https://mlsanity.com/paper/2109.04094/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Federated learning with class imbalance reduction](https://mlsanity.com/paper/2011.11266/) (2020)
- [Fed-Focal Loss for imbalanced data classification in Federated Learning](https://mlsanity.com/paper/2011.06283/) (2020)
- [Addressing Class Imbalance in Federated Learning](https://mlsanity.com/paper/2008.06217/) (2020)

## Built on by (most cited first, 7 of 7)

- [A Survey on Class Imbalance in Federated Learning](https://mlsanity.com/paper/2303.11673/) (2023)
- [A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs](https://mlsanity.com/paper/2310.13161/) (2023)
- [Using adversarial images to improve outcomes of federated learning for non-IID data](https://mlsanity.com/paper/2206.08124/) (2022)
- [On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning](https://mlsanity.com/paper/2410.21192/) (2024)
- [pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data](https://mlsanity.com/paper/2511.18066/) (2025)
- [CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2505.23849/) (2025)
- [Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data](https://mlsanity.com/paper/2507.22488/) (2025)

## Abstract (arXiv)

Federated learning is a distributed machine learning paradigm that trains a global model for prediction based on a number of local models at clients while local data privacy is preserved. Class imbalance is believed to be one of the factors that degrades the global model performance. However, there has been very little research on if and how class imbalance can affect the global performance. class imbalance in federated learning is much more complex than that in traditional non-distributed machine learning, due to different class imbalance situations at local clients. Class imbalance needs to be re-defined in distributed learning environments. In this paper, first, we propose two new metrics to define class imbalance -- the global class imbalance degree (MID) and the local difference of class imbalance among clients (WCS). Then, we conduct extensive experiments to analyze the impact of class imbalance on the global performance in various scenarios based on our definition. Our results show that a higher MID and a larger WCS degrade more the performance of the global model. Besides, WCS is shown to slow down the convergence of the global model by misdirecting the optimization.