  Federated Learning (FL) is an emerging machine learning paradigm that enables
multiple clients to jointly train a model to take benefits from diverse
datasets from the clients without sharing their local training datasets. FL
helps reduce data privacy risks. Unfortunately, FL still exist several issues
regarding privacy and security. First, it is possible to leak sensitive
information from the shared training parameters. Second, malicious clients can
collude with each other to steal data, models from regular clients or corrupt
the global training model. To tackle these challenges, we propose SecFL - a
confidential federated learning framework that leverages Trusted Execution
Environments (TEEs). SecFL performs the global and local training inside TEE
enclaves to ensure the confidentiality and integrity of the computations
against powerful adversaries with privileged access. SecFL provides a
transparent remote attestation mechanism, relying on the remote attestation
provided by TEEs, to allow clients to attest the global training computation as
well as the local training computation of each other. Thus, all malicious
clients can be detected using the remote attestation mechanisms.
