Data heterogeneity is an inherent challenge that hinders the performance of federated learning (FL). Recent studies have identified the biased classifiers of local models as the key bottleneck. Previous attempts have used classifier calibration after FL training, but this approach falls short in improving the poor feature representations caused by training-time classifier biases. Resolving the classifier bias dilemma in FL requires a full understanding of the mechanisms behind the classifier. Recent advances in neural collapse have shown that the classifiers and feature prototypes under perfect training scenarios collapse into an optimal structure called simplex equiangular tight frame (ETF). Building on this neural collapse insight, we propose a solution to the FL's classifier bias problem by utilizing a synthetic and fixed ETF classifier during training. The optimal classifier structure enables all clients to learn unified and optimal feature representations even under extremely heterogeneous data. We devise several effective modules to better adapt the ETF structure in FL, achieving both high generalization and personalization. Extensive experiments demonstrate that our method achieves state-of-the-art performances on CIFAR-10, CIFAR-100, and Tiny-ImageNet.

翻译：数据异构性是阻碍联邦学习性能的内在挑战。近期研究已将局部模型的有偏分类器识别为关键瓶颈。现有方法在联邦训练后采用分类器校准，但这种方式无法改善因训练阶段分类器偏差导致的劣质特征表示。解决联邦学习中的分类器偏差困境需要深入理解分类器背后的运行机制。神经坍缩的最新进展表明，在完美训练场景下，分类器与特征原型会坍缩成名为单纯形等角紧框架的最优结构。基于这一神经坍缩洞见，我们提出通过训练期间使用合成固定ETF分类器来解决联邦学习分类器偏差问题的方案。该最优分类器结构使所有客户端即使在极度异构数据下也能学习统一的优质特征表示。我们设计了若干有效模块以更好地适配联邦学习中的ETF结构，实现了通用化与个性化性能的双重提升。大量实验证明，本方法在CIFAR-10、CIFAR-100及Tiny-ImageNet数据集上均达到了最优性能。