Label noise analysis meets adversarial training: A defense against label poisoning in federated learning

Author:

Hallaji EhsanORCID,Razavi-Far Roozbeh,Saif Mehrdad,Herrera-Viedma Enrique

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference30 articles.

1. A survey on federated learning;Zhang;Knowl.-Based Syst.,2021

2. Federated machine learning: Concept and applications;Yang;ACM Trans. Intell. Syst. Technol.,2019

3. J. Konečný, H.B. McMahan, F.X. Yu, P. Richtarik, A.T. Suresh, D. Bacon, Federated Learning: Strategies for Improving Communication Efficiency, in: NIPS Workshop on Private Multi-Party Machine Learning, 2016, arXiv:1610.05492.

4. Towards privacy-preserving and verifiable federated matrix factorization;Wan;Knowl.-Based Syst.,2022

5. Federated adversarial domain generalization network: A novel machinery fault diagnosis method with data privacy;Wang;Knowl.-Based Syst.,2022

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