NOMAI: Scalable Federated Learning with Private Weights

Reported by arXiv cs.CR (Yiyi Sun, Mushtari Sadia, Amrita Roy Chowdhury)

Summary by Cyber Security News16 Oct 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.21059

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.

Read the full article at arXiv cs.CR →

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