FedEach:以评估者为基础的激励机制为人类活动认可的联合学习
Hyun Woo Lim1, Sean Yonathan Tanjung1, Ignatius Iwan1
1Department of Industrial and Management Engineering, Hankuk University of Foreign Studies, Yongin 17035, Republic of Korea.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
联合学习 (FL) 面临着恶意客户端的挑战. "FedEach"框架使用评估器来识别可靠的参与者,提高全球模型的准确性,而不需要服务器标记的数据.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 分散式系统 分散式系统 分散式系统
背景情况:
- 联合学习 (FL) 允许在没有数据共享的情况下进行协作模式培训.
- 拜占庭客户对FL的完整性和性能构成重大威胁.
- 现有的针对恶意客户端的FL防御通常需要服务器标记的数据或缺乏对可靠客户端的激励.
研究的目的:
- 引入一个强大的联合学习框架,FedEach,有效地处理拜占庭客户.
- 开发一个基于评估者的激励机制,识别和利用可靠的客户,而不依赖于服务器标记的数据.
- 为了确保实际FL应用的计算效率.
主要方法:
- 一个新的联合学习框架 (FedEach) 具有基于评估者的激励机制.
- 基于服务器的评价者和参与者的选择,使用绩效标准 (测试成绩,声誉).
- 评估员驱动的客户可靠性的评估 (恶意与可靠).
- 仅从确定可靠的参与者和评估者的模型聚合.
- 客户贡献计算,以获得公平的认可和惩罚.
主要成果:
- 对于恶意客户端来说,FedEach表现出了显著的稳定性,特别是在高恶意环境中.
- 该框架有效地汇总了可靠客户的更新,提高了全球模型的性能.
- FedEach保持了计算效率,适合实时FL应用程序.
- 人类活动识别 (HAR) 数据集的经验验证证证实了该框架的有效性.
结论:
- 在拜占庭客户的存在下,FedEach为联合学习提供了一个强大的,与激励相关的解决方案.
- 基于评估者的机制提高了安全性和可靠性,而不增加时间复杂性或要求公开验证数据集.
- FedEach是一个实用和高效的框架,用于现实世界FL应用程序,如基于传感器的HAR.
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