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An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning

  • Published:2023 Issue: Volume: Page:1-13
  • ISSN:2332-7790
  • Container-title:IEEE Transactions on Big Data
  • language:
  • Short-container-title:IEEE Trans. Big Data

Author:

Li Shenghui1ORCID,Ngai Edith C.-H.2ORCID,Voigt Thiemo3ORCID

Affiliation:

1. Department of Information Technology, Uppsala University, Uppsala, Sweden

2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

3. Department of Electrical Engineering, Uppsala University, Uppsala, Sweden

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Information Systems and Management,Information Systems

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. XGDFed: Exposing Vulnerability in Byzantine Robust Federated Binary Classifiers with Novel Aggregation-Agnostic Model Poisoning Attack;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

2. PersistVerify: Federated model ownership verification with spatial attention and boundary sampling;Knowledge-Based Systems;2024-06

3. Demo Abstract: Blades: A Unified Benchmark Suite for Byzantine-Resilient in Federated Learning;2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI);2024-05-13

4. Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning;2024 IEEE/ACM Ninth International Conference on Internet-of-Things Design and Implementation (IoTDI);2024-05-13

5. FedSuper: A Byzantine-Robust Federated Learning Under Supervision;ACM Transactions on Sensor Networks;2024-01-10
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