Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning

This research proposes ByITFL and LoByITFL, two new federated learning (FL, a method where multiple computers train an AI model together without sharing raw data) schemes that protect user privacy while defending against Byzantine users (participants who send corrupted or malicious data). ByITFL uses Lagrange coded computing (a technique that spreads data across multiple servers to protect it) and re-randomization to achieve perfect privacy but requires significant communication overhead, while LoByITFL reduces communication costs but requires a Trusted Third Party (TTP, an external organization that users must trust) for one-time setup before training begins.