  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.
