While the Internet of Things (IoT) can benefit from machine learning by outsourcing model training on the cloud, user data exposure to an untrusted cloud service provider can pose threat to user privacy. Recently, federated learning is proposed as an approach for privacy-preserving machine learning (PPML) for the IoT, while its practicability remains unclear. This work presents the evaluation on the efficiency and privacy performance of a readily available federated learning framework based on PySyft, a Python library for distributed deep learning. It is observed that the training speed of the framework is significantly slower than of the centralized approach due to communication overhead. Meanwhile, the framework bears some vulnerability to potential man-in-the-middle attacks at the network level. The report serves as a starting point for PPML performance analysis and suggests the future direction for PPML framework development.
翻译:虽然通过对云层进行外包示范培训可以使Things Internet(IoT)从机器学习中受益,但用户数据接触一个不信任的云层服务供应商可能会对用户隐私造成威胁。最近,提议将联合学习作为IoT的隐私保护机器学习(PPML)的一种方法,但其实用性尚不清楚。这项工作对基于PySyft的现成的联邦学习框架的效率和隐私表现进行了评价,PySyft是一个用于分散深度学习的Python图书馆。据观察,由于通信管理,该框架的培训速度大大慢于集中方法的速度。同时,该框架在网络一级对中层的潜在人攻击具有某种脆弱性。该报告作为PML业绩分析的起点,并提出了PPML框架开发的未来方向。