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.

翻译：本文审议了一个由中央协调服务器和多种分布式当地工人组成的联合学习系统,他们都能够进入可信赖的执行环境(TEEs),为确保不受信任的工人正确完成当地学习,我们提议采用新的TEE方法,同时结合应用加密技术、智能合同和游戏理论。 理论分析和基于执行的评价表明,拟议的方法是安全、高效和实用的。