针对异质数据的个性化联合学习:一种分布式边缘集群方法
Muhammad Firdaus1, Siwan Noh2, Zhuohao Qian2
1Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Republic of Korea.
个性化联合学习 (PFL) 提高了与异质数据的模型融合. 本研究介绍了PFL (BPFL) 的区块链支持的分布式边缘集群,以提高隐私,安全和实时性能.
科学领域:
- 分布式机器学习 分布式机器学习
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 允许跨设备的协作模式培训,同时保持数据隐私.
- 在FL中的数据异质性导致模型趋同差,需要个性化联合学习 (PFL).
- 现有的基于集群的PFL方法通常依赖于集中式服务器协调.
研究的目的:
- 解决集中式PFL方法的局限性.
- 为PFL (BPFL) 引入一个新的区块链支持的分布式边缘集群.
- 为了提高PFL系统的隐私,安全和实时性能.
主要方法:
- 集成区块链技术以实现安全,不变的交易记录,改善客户端选择和集群.
- 利用边缘计算进行本地化存储和计算,使处理更接近客户端.
- 开发一个分散的架构,用于PFL协调.
主要成果:
- 拟议的BPFL框架结合了区块链和边缘计算的好处,以改善PFL.
- 通过区块链的分布式分类账来增强客户隐私和安全性.
- 由于边缘计算靠近客户端,改善了实时服务和低延迟通信.
结论:
- BPFL提供了一个有前途的去中心化方法来克服个性化联合学习的挑战.
- 区块链和边缘计算的整合显著提高了PFL的效率和安全性.
- 需要进一步的研究来开发数据集,以评估BPFL协议中的攻击和防御.
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