一个轻量级的增强隐私的联邦集群算法,用于边缘计算
Jun Wang1, Xianghua Chen1, Xing Cheng2
1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了一种增强隐私的联合k-means集群算法,使用局部敏感散列用于边缘计算. 它有效地处理非IID数据,并降低通信开销,同时保护数据隐私.
科学领域:
- 边缘计算 边缘计算
- 数据挖掘 数据挖掘
- 机器学习 机器学习
背景情况:
- 边缘计算中的分布式数据是分散的,异质的和对隐私敏感的.
- 联合集群面临诸如高通讯开销,非IID数据和隐私风险等挑战.
研究的目的:
- 为边缘计算提出一个增强隐私的联合k-means集群算法.
- 为应对非IID数据,通信开销和隐私泄露的挑战.
主要方法:
- 利用局部敏感哈希 (LSH) 进行集群中心的隐私保护加密.
- 实现一个单一的客户端到服务器通信协议.
- 在服务器上的加密空间中执行二次加权k-means集群.
主要成果:
- 该算法有效地减轻了非IID数据问题,同时保持了隐私.
- 在单一的沟通环节中实现全球聚类,减少开销.
- 在MNIST和CIFAR-10数据集上表现出强大的集群性能.
结论:
- 拟议的算法为边缘环境中的分布式数据挖掘提供了一个高效,可适应和保护隐私的解决方案.
- 它在通信受限制的设置中增强了实用性,而不需要依赖可信服务器.
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