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固有挑战。近期研究将局部模型中的偏置分类器识别为关键瓶颈。以往方法尝试在联邦训练后进行分类器校准，但该方案在改善训练期间由分类器偏差导致的低质特征表示方面存在不足。解决联邦学习中的分类器偏差困境需要全面理解分类器背后的运行机制。神经坍缩领域的最新进展表明，在理想训练场景下，分类器与特征原型会坍缩为称为单纯形等角紧框架（ETF）的最优结构。基于这一神经坍缩洞察，我们提出通过训练中采用合成且固定的ETF分类器来解决联邦学习的分类器偏差问题。该最优分类器结构使所有客户端即使在极端异构数据条件下也能学习到统一且最优的特征表示。我们设计了多个有效模块以更好地适配联邦学习中的ETF结构，同时实现高泛化性与个性化。大量实验证明，我们的方法在CIFAR-10、CIFAR-100及Tiny-ImageNet数据集上达到了最先进的性能水平。