相关实验视频
联合学习中的物联网身份验证:方法,挑战和未来方向
Arwa Badhib1, Suhair Alshehri1, Asma Cherif1,2
1Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
联合学习 (FL) 能够在相互连接的设备上进行安全的数据分析. 本研究调查了FL的身份验证方法,解决了诸如模型中毒等安全威胁,并加强了对人工智能系统的信任.
科学领域:
- 物联网 (IoT) 安全问题
- 机器学习隐私 机器学习隐私
- 分布式人工智能 分布式人工智能
背景情况:
- 物联网产生了大量数据,通过机器学习进行分析,但集中式培训引发了隐私问题.
- 联邦学习 (FL) 在本地培训模型,只分享更新,减轻隐私问题.
- FL系统面临着模型中毒和拜占庭式攻击等安全威胁,需要强大的身份验证.
研究的目的:
- 为联邦学习 (FL) 中的认证机制提供全面的调查.
- 检查FL认证流程,挑战和架构方面的考虑.
- 根据技术和系统背景对现有的FL认证方案进行分类.
主要方法:
- 审查和评估现有的FL认证方案.
- 基于启用技术 (区块链,密码学,人工智能) 和系统背景的方案的分类.
- 分析数据集,实验环境,并识别研究缺口.
主要成果:
- 现有的认证方案在有效性,局限性和实用性方面各不相同.
- 分类提供了当前方法的结构化概述.
- 确定了开放研究的挑战和未来的方向,以确保安全的FL.
结论:
- 强大的身份验证对于安全可信的联合学习至关重要.
- 这项调查为推进FL安全提供了基础参考.
- 需要进一步的研究来应对已识别的挑战,并提高FL的稳定性.
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