基于量子化的链接,保护隐私的联合学习
Ya Liu1,2, Shumin Wu3, Yibo Li3
1The Department of Computer Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. liuya@usst.edu.cn.
Scientific reports
|May 16, 2025
概括
本研究介绍了Q-Chain FL,这是一个新的联合学习 (FL) 框架,可以提高隐私和效率. Q-Chain FL显著降低了分布式机器学习应用程序的通信开销和计算成本.
科学领域:
- 分布式机器学习 分布式机器学习
- 数据隐私和安全数据隐私和安全
背景情况:
- 联邦学习 (FL) 通过在当地培训模型来保护数据隐私.
- 传统的FL面临的挑战是通信效率,计算成本和隐私保护,特别是在边缘计算中.
- 高昂的开销阻碍了当前FL计划中的实时应用.
研究的目的:
- 提出一个创新的联合学习框架,Q-Chain FL.
- 解决传统FL的通信和计算开销挑战.
- 在分布式学习中增强隐私保护和模型融合速度.
主要方法:
- 将量子化压缩技术集成到链式FL架构 (Q-Chain FL) 中.
- 在用户节点有效压缩和传输模型参数差异.
- 在服务器节点上的参数的无解压和聚合.
主要成果:
- 在Q-Chain FL中,通信和计算开销较低.
- 该框架实现了跨多个数据集 (MNIST,CIFAR-10,CelebA) 的快速融合速度和高安全性.
- 通信开销减少了约62.5% (相对于FedAvg) 和44.7% (相对于链式PPFL).
结论:
- Q-Chain FL为联合学习提供了一个强大的,可适应的解决方案.
- 拟议的框架有效地减轻了隐私风险,同时提高了效率.
- 结果突出了Q-Chain FL在现实世界的分布式学习场景中的潜力.
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