Federated Learning with Noisy Labels: Achieving Generalization in the Face of Label NoiseDownload PDF

06 Oct 2022 (modified: 05 May 2023)INTERPOLATE at NeurIPS 2022Readers: Everyone
Keywords: federated learning, noisy labels, label correction, deep learning, label correction
Abstract: Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets, where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are readily available on users' devices; in reality, label noise can naturally occur in FL and follows a non-i.i.d. distribution among clients. Due to the ``non-iid-ness'' challenges, existing state-of-the-art centralized approaches exhibit unsatisfactory performance, while previous FL studies rely on data exchange or repeated server-side aid to improve model's performance. Here, we propose FedLN, a framework to deal with label noise across different FL training stages; namely, FL initialization, and server-side model aggregation. Extensive experiments on various publicly available vision and audio datasets demonstrate an improvement of 24% on average compared to state-of-the-art methods for a label noise level of 70%.
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