Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning

Summary

Researchers propose ByITFL and LoByITFL, two federated learning schemes that resist Byzantine users while keeping eavesdroppers from learning private user data. Both rely on a small representative dataset held by the federator and a discriminator function that mitigates corrupt users' contributions. ByITFL uses Lagrange coded computing and re-randomization to achieve perfect Information-Theoretic privacy at a significant communication cost, while LoByITFL reduces that cost but needs a Trusted Third Party for a one-time initialization before training.