基于 TEE 的联合学习系统中本地工人的选择性测试

本文考虑了一个由中央协调服务器和多个分布式本地工作人员组成的联合学习系统，所有这些人员都可以访问受信任的执行环境 （TEEs）。为了确保不受信任的工人正确执行本地学习，我们提出了基于 TEE 的新方法，该方法还结合了应用加密技术、智能合约和博弈论技术。理论分析和基于执行的评价表明，拟议的方法是安全、有效和实用的。

原文题目：TEE-based Selective Testing of Local Workers in Federated Learning Systems

原文：This paper considers a federated learning system composed of a central coordinating server and multiple distributed local workers, all having access to trusted execution environments (TEEs). In order to ensure that the untrusted workers correctly perform local learning, we propose a new TEE-based approach that also combines techniques from applied cryptography, smart contract and game theory. Theoretical analysis and implementation-based evaluations show that, the proposed approach is secure, efficient and practical.