NOMAI: Scalable Federated Learning with Private Weights
Yiyi Sun, Mushtari Sadia, Amrita Roy Chowdhury
Abstract
The twin goals of privacy-preserving federated learning (FL) -- harnessing distributed data while protecting the parties who contribute it -- demands three properties: model privacy, data privacy, and integrity. Achieving all three in a realistic deployment -- with a single coordinator and millions of users -- remains an open challenge that prior work has yet to fully resolve. We present NOMAI, the first scalable MPC-based FL system to provide all three guarantees simultaneously. NOMAI operates under an honest-majority assumption in the semi-honest model, with an extension to the malicious-with-abort setting. At its core, NOMAI combines a multi-committee MPC architecture with packed Shamir secret sharing, enabling SIMD-style secure computation that scales far beyond what monolithic n-party protocols can support. We further introduce ML-aware optimizations and new cryptographic primitives that substantially reduce per-user overhead. Our evaluation shows NOMAI is practically deployable at unprecedented scale: it reduces per-user computation to just <350 ms (semi-honest) and <700 ms (malicious) for per iteration of training MNIST, achieving three orders-of-magnitude improvements over prior work while seamlessly supporting populations of over 106 users. This demonstrates, for the first time, that MPC-based FL can meet the scale demands of real-world systems without compromising on privacy or integrity.