most citedTowards Federated Learning against Noisy Labels via Local Self-Regularization

63 citations · 90 across the 2 of their papers we have counts for

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cs.LG2024★ 27 cited

Tackling Noisy Clients in Federated Learning with End-to-end Label Correction

Xuefeng Jiang, Sheng Sun, Jia Li +6

Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive applications without sacrificing the sensitive private information of clients. However,…

cs.LG2022★ 63 cited

Towards Federated Learning against Noisy Labels via Local Self-Regularization

Xuefeng Jiang, Sheng Sun, Yuwei Wang +1

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, since high-quality labe…