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Cited by 65Privacy-Preserving Technologies in DataMachine Learning and Data ClassificationDomain Adaptation and Few-Shot Learning

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Towards Federated Learning against Noisy Labels via Local Self-Regularization

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Jiang, Xuefeng, Sun, Sheng, Wang, Yuwei, Liu, Min·2022·Proceedings of the 31st ACM International Conference on Information & Knowledge Management
DOI 10.1145/3511808.3557475
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Abstract

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, data with noisy labels are ubiquitous in reality since high-quality labeled data require expensive human efforts, which cause severe performance degradation. Although a lot of methods are proposed to directly deal with noisy labels, these methods either require excessive computation overhead or violate the privacy protection principle of FL. To this end, we focus on this issue in FL with the purpose of alleviating performance degradation yielded by noisy labels meanwhile guaranteeing data privacy. Specifically, we propose a Local Self-Regularization method, which effectively regularizes the local training process via implicitly hindering the model from memorizing noisy labels and explicitly narrowing the model output discrepancy between original and augmented instances using self distillation. Experimental results demonstrate that our proposed method can achieve notable resistance against noisy labels in various noise levels on three benchmark datasets. In addition, we integrate our method with existing state-of-the-arts and achieve superior performance on the real-world dataset Clothing1M.The code is available at https://github.com/Sprinter1999.

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Metadata

Authors
Jiang, X., Sun, S., Wang, Y., & Liu, M.
Affiliations
Chinese Academy of Sciences (CN)
ORCID
Venue
Proceedings of the 31st ACM International Conference on Information & Knowledge Management
Publisher
ACM
Published
2022-10-17
Pages
862-873
ISSN
2155-0751 (ISSN-L)
DOI
10.1145/3511808.3557475
OpenAlex
W4293791267
Cited by
65
References
5
Citations by year
2026: 18 · 2025: 22 · 2024: 14 · 2023: 11
FWCI
6.50
Type
conference paper
Language
en
Open access
Licence
Locations
  • ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion, other-oa)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
The National Natural Science Foundation of China
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords
federated learning, label noise, self-distillation, label noise robustness, privacy preservation, data memorization, image classification

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CoChat. (2026). Citation check: Towards Federated Learning against Noisy Labels via Local Self-Regularization. https://app.cochat.ai/papers/10.1145/3511808.3557475
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Jiang, X., Sun, S., Wang, Y., & Liu, M. (2022). Towards Federated Learning against Noisy Labels via Local Self-Regularization. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. https://doi.org/10.1145/3511808.3557475