NOMAI: Scalable Federated Learning with Private Weights
NOMAI introduces a scalable Multi-Party Computation (MPC) system for Federated Learning that simultaneously ensures model privacy, data privacy, and data integrity. It solves the challenge of scaling FL to millions of users by using a committee-based architecture combined with Packed Shamir Secret Sharing and ML-aware optimizations. This allows it to achieve high performance while maintaining strong security guarantees.