Defense Strategy against Byzantine Attacks in Federated Machine Learning: Developments towards Explainability-Reference-Cited by-同舟云学术

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Defense Strategy against Byzantine Attacks in Federated Machine Learning: Developments towards Explainability

Published:2024-06-30 Issue: Volume:55 Page:1-8
ISSN:
Container-title:2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Author:

Rodríguez-Barroso Nuria¹,Del Ser Javier¹,Luzón M. Victoria²,Herrera Francisco¹

Affiliation:

1. Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada,Dept. of Computer Science and Artificial Intelligence,Granada,Spain,18071

2. University of Granada,Department of Software Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI),Granada,Spain,18071

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609967/10611747/10611769.pdf?arnumber=10611769

Reference32 articles.

1. Regulation of the European Parliament and of the Council,2021

2. Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation

3. Trustworthy AI: From principles to practices;Li;ACM Computing Surveys,2023

4. Federated Learning

5. A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends

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