Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients

• URL: http://arxiv.org/abs/2304.02892v1
• Date: Thu, 6 Apr 2023 06:47:14 GMT
• Title: Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
• Authors: Chenrui Wu, Zexi Li, Fangxin Wang, Chao Wu
• Abstract summary: Federated learning (FL) is a distributed framework for collaboratively training with privacy guarantees. In real-world scenarios, clients may have Non-IID data (local class imbalance) with poor annotation quality (label noise) We propose FedCNI without using an additional clean proxy dataset. It includes a noise-resilient local solver and a robust global aggregator.
• Score: 4.782145666637457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) is a distributed framework for collaboratively training with privacy guarantees. In real-world scenarios, clients may have Non-IID data (local class imbalance) with poor annotation quality (label noise). The co-existence of label noise and class imbalance in FL's small local datasets renders conventional FL methods and noisy-label learning methods both ineffective. To address the challenges, we propose FedCNI without using an additional clean proxy dataset. It includes a noise-resilient local solver and a robust global aggregator. For the local solver, we design a more robust prototypical noise detector to distinguish noisy samples. Further to reduce the negative impact brought by the noisy samples, we devise a curriculum pseudo labeling method and a denoise Mixup training strategy. For the global aggregator, we propose a switching re-weighted aggregation method tailored to different learning periods. Extensive experiments demonstrate our method can substantially outperform state-of-the-art solutions in mix-heterogeneous FL environments.

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