NOMAI: Scalable Federated Learning with Private Weights

본문 미리보기

arXiv:2610.21059v1 Announce Type: new 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