Noisy labels in distributed datasets induce severe local overfitting and consequently compromise the global model in federated learning (FL). Most existing solutions rely on selecting clean devices or aligning with public clean datasets, rather than endowing the model itself with robustness. In this paper, we propose FedCova, a dependency-free federated covariance learning framework that eliminates such external reliances by enhancing the model's intrinsic robustness via a new perspective on feature covariances. Specifically, FedCova encodes data into a discriminative but resilient feature space to tolerate label noise. Built on mutual information maximization, we design a novel objective for federated lossy feature encoding that relies solely on class feature covariances with an error tolerance term. Leveraging feature subspaces characterized by covariances, we construct a subspace-augmented federated classifier. FedCova unifies three key processes through the covariance: (1) training the network for feature encoding, (2) constructing a classifier directly from the learned features, and (3) correcting noisy labels based on feature subspaces. We implement FedCova across both symmetric and asymmetric noisy settings under heterogeneous data distribution. Experimental results on CIFAR-10/100 and real-world noisy dataset Clothing1M demonstrate the superior robustness of FedCova compared with the state-of-the-art methods.

翻译：分布式数据集中的噪声标签会引发严重的局部过拟合，进而损害联邦学习（FL）中的全局模型。现有解决方案大多依赖于选择干净设备或与公开干净数据集对齐，而非赋予模型自身鲁棒性。本文提出FedCova，一种无依赖的联邦协方差学习框架，通过从特征协方差的新视角增强模型内在鲁棒性，从而消除此类外部依赖。具体而言，FedCova将数据编码至一个具有判别力且对标签噪声具备容忍性的特征空间。基于互信息最大化原理，我们设计了一种新颖的联邦有损特征编码目标函数，该函数仅依赖于类别特征协方差并包含误差容忍项。利用协方差表征的特征子空间，我们构建了一个子空间增强的联邦分类器。FedCova通过协方差统一了三个关键过程：（1）训练特征编码网络，（2）直接从学习到的特征构建分类器，以及（3）基于特征子空间校正噪声标签。我们在异构数据分布下的对称与非对称噪声设置中实现了FedCova。在CIFAR-10/100及真实噪声数据集Clothing1M上的实验结果表明，相较于现有先进方法，FedCova具有更优越的鲁棒性。