Federated Learning (FL) is a distributed machine learning paradigm that enables collaboration among multiple clients to train a shared model without sharing raw data. However, a major challenge in FL is the label imbalance, where clients may exclusively possess certain classes while having numerous minority and missing classes. Previous works focus on optimizing local updates or global aggregation but ignore the underlying imbalanced label distribution across clients. In this paper, we propose a novel approach ReGL to address this challenge, whose key idea is to Recover the Global data distribution Locally. Specifically, each client uses generative models to synthesize images that complement the minority and missing classes, thereby alleviating label imbalance. Moreover, we adaptively fine-tune the image generation process using local real data, which makes the synthetic images align more closely with the global distribution. Importantly, both the generation and fine-tuning processes are conducted at the client-side without leaking data privacy. Through comprehensive experiments on various image classification datasets, we demonstrate the remarkable superiority of our approach over existing state-of-the-art works in fundamentally tackling label imbalance in FL.

本研究解决了联邦学习中的标签不平衡问题，即不同客户端之间可能存在部分类标签缺失的问题。我们提出了一种创新方法ReGL，通过生成模型在客户端合成补充缺失标签的图像，从而缓解标签不平衡。此外，我们的实验结果显示，该方法在多种图像分类数据集上显著优于现有技术，对联邦学习中的标签不平衡问题具有深远的影响。

在联邦学习中局部恢复全球数据分布