TEE-based Selective Testing of Local Workers in Federated Learning Systems
This addresses security and trust issues in federated learning for distributed systems, but it appears incremental as it combines existing techniques like TEEs, cryptography, smart contracts, and game theory.
The paper tackles the problem of ensuring correct local learning by untrusted workers in federated learning systems using trusted execution environments (TEEs), and the result is a secure, efficient, and practical approach validated through theoretical analysis and implementation-based evaluations.
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.