边缘计算中的联合学习:漏洞,攻击和防御-一项调查
Sahar Alhawas1, Murad A Rassam1
1Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
结合边缘计算 (EC) 的联合学习 (FL) 提供了隐私和效率,但面临安全风险. 本调查审查了边缘环境中强大的FL的漏洞和防御.
科学领域:
- 分布式机器学习 分布式机器学习
- 边缘计算 边缘计算
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 允许在不共享原始数据的情况下进行协作培训,增强隐私并减少通信开销.
- 将FL与边缘计算 (EC) 集成,通过将计算更接近数据,在资源有限的环境中促进实时,低延迟的决策.
- 在FL和EC的去中心化引入了诸如数据中毒,后门攻击,推断泄露和拜占庭式行为等重大漏洞,这些漏洞因设备异质性和连接性问题而加剧.
研究的目的:
- 为联邦学习和边缘计算的交叉提供全面的审查.
- 专注于识别和分析EC环境中与FL相关的漏洞,攻击载体和防御机制.
- 在现实世界的边缘部署中为安全,私有和高效的FL系统提供未来研究方向的信息.
主要方法:
- 对联邦学习和边缘计算的现有文献进行系统审查.
- 分析当前的防御策略,包括强大的聚合,异常检测,差异隐私和安全聚合.
- 在边缘环境的约束下评估这些防御的可行性和适用性.
主要成果:
- 确定了分散的FL和EC系统固有的关键漏洞和攻击载体.
- 分析了现有的防御机制在应对这些威胁方面的有效性和局限性.
- 强调了边缘部署中设备异质性和间歇性连接所带来的挑战.
结论:
- FL和EC的组合是一个有希望的范式,但需要强有力的安全和隐私措施.
- 现有的防御系统显示出潜力,但需要适应边缘环境的独特挑战.
- 进一步的研究对于开发可扩展,弹性和节能解决方案至关重要,以确保EC的安全FL.
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