[2603.04062] FedCova: Robust Federated Covariance Learning Against Noisy Labels

Computer Science > Machine Learning arXiv:2603.04062 (cs) [Submitted on 4 Mar 2026] Title:FedCova: Robust Federated Covariance Learning Against Noisy Labels Authors:Xiangyu Zhong, Xiaojun Yuan, Ying-Jun Angela Zhang View a PDF of the paper titled FedCova: Robust Federated Covariance Learning Against Noisy Labels, by Xiangyu Zhong and 2 other authors View PDF HTML (experimental) Abstract: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...