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

TEE-based Selective Testing of Local Workers in Federated Learning Systems