Federated learning offers a paradigm to the challenge of preserving privacy in distributed machine learning. However, datasets distributed across each client in the real world are inevitably heterogeneous, and if the datasets can be globally aggregated, they tend to be long-tailed distributed, which greatly affects the performance of the model. The traditional approach to federated learning primarily addresses the heterogeneity of data among clients, yet it fails to address the phenomenon of class-wise bias in global long-tailed data. This results in the trained model focusing on the head classes while neglecting the equally important tail classes. Consequently, it is essential to develop a methodology that considers classes holistically. To address the above problems, we propose a new method FedLF, which introduces three modifications in the local training phase: adaptive logit adjustment, continuous class centred optimization, and feature decorrelation. We compare seven state-of-the-art methods with varying degrees of data heterogeneity and long-tailed distribution. Extensive experiments on benchmark datasets CIFAR-10-LT and CIFAR-100-LT demonstrate that our approach effectively mitigates the problem of model performance degradation due to data heterogeneity and long-tailed distribution. our code is available at https://github.com/18sym/FedLF.

本研究旨在解决传统联邦学习方法未能有效应对全局长尾数据的类别偏差问题，从而导致模型在处理头类别时忽视尾类别。我们提出了一种新方法FedLF，通过自适应Logit调整、连续类中心优化和特征去相关来优化本地训练阶段，实验证明该方法能够有效缓解数据异构性和长尾分布导致的模型性能下降。

FedLF：联邦长尾学习中的自适应Logit调整与特征优化