Threats and Defenses in the Federated Learning Life Cycle: A Comprehensive Survey and Challenges

IF 10.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE transactions on neural networks and learning systems Pub Date : 2025-05-15 DOI:10.1109/tnnls.2025.3563537

Yanli Li, Zhongliang Guo, Nan Yang, Huaming Chen, Dong Yuan, Weiping Ding

引用次数: 0

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联邦学习生命周期中的威胁与防御：综合调查与挑战

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

CiteScore

23.80

自引率

9.60%

发文量

2102

审稿时长

3-8 weeks

期刊介绍： The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.