Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manner. However, the data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model severely biased to the head classes with the majority of the training samples. To alleviate this issue, decoupled training has recently been introduced to FL, considering it has achieved promising results in centralized long-tailed learning by re-balancing the biased classifier after the instance-balanced training. However, the current study restricts the capacity of decoupled training in federated long-tailed learning with a sub-optimal classifier re-trained on a set of pseudo features, due to the unavailability of a global balanced dataset in FL. In this work, in order to re-balance the classifier more effectively, we integrate the local real data with the global gradient prototypes to form the local balanced datasets, and thus re-balance the classifier during the local training. Furthermore, we introduce an extra classifier in the training phase to help model the global data distribution, which addresses the problem of contradictory optimization goals caused by performing classifier re-balancing locally. Extensive experiments show that our method consistently outperforms the existing state-of-the-art methods in various settings.

翻译：联邦学习（FL）已成为一种流行的分布式学习范式，它允许多个客户端以保护数据隐私的方式协同训练全局模型。然而，现实世界中的数据样本通常遵循长尾分布，而基于分散且长尾数据的联邦学习会导致全局模型性能不佳，严重偏向于拥有大部分训练样本的头部类别。为缓解此问题，解耦训练近期被引入联邦学习，考虑到它在集中式长尾学习中已取得显著成果——通过在实例均衡训练后对偏斜分类器进行再平衡。然而，当前研究限制了联邦长尾学习中解耦训练的能力：由于联邦学习中无法获得全局均衡数据集，分类器仅在一组伪特征上重新训练，导致次优性能。为更有效地再平衡分类器，本文融合局部真实数据与全局梯度原型构建局部均衡数据集，从而在局部训练过程中实现分类器再平衡。此外，我们在训练阶段引入额外分类器以辅助建模全局数据分布，从而解决局部执行分类器再平衡时产生的优化目标矛盾问题。大量实验表明，本方法在多种设置下均持续优于现有最先进方法。