基于贝叶斯优化模型中毒的联邦学习不确定性最大化

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-17 DOI:10.1109/tifs.2025.3531143

Marios Aristodemou, Xiaolan Liu, Yuan Wang, Konstantinos G. Kyriakopoulos, Sangarapillai Lambotharan, Qingsong Wei

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Transactions on Information Forensics and Security 工程技术-工程：电子与电气

CiteScore

14.40

自引率

7.40%

发文量

234

审稿时长

6.5 months

期刊介绍： The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features