Homecs.CRarXiv:2605.30123

Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channels

cs.CRMachine Learning2026-05v1license

Abstract

Homomorphic encryption (HE) enables privacy-preserving aggregation in federated learning (FL) by allowing the server to operate on encrypted data without decryption. Existing HE-over-the-air methods mainly rely on single-key HE schemes and require channel estimation or pre-equalization to compensate for wireless fading. However, single-key HE remains vulnerable to honest-but-curious clients sharing the same secret key. In addition, compromising a single client may compromise the security of the entire network, while multi-key HE schemes provide stronger client-level security by assigning each device its own secret key. We propose a four-phase protocol that enables xMK-CKKS, a famous multi-key HE scheme, aggregation over a shared wireless channel without channel estimation. The protocol retransmits partial public keys and ciphertexts through the same channel realization, so that the dominant large-modulus encryption terms cancel algebraically during decryption. We integrate this protocol with zero-order FL over slowly varying LoS-dominant channels, where each device transmits a single encrypted scalar per round and the communication/encryption overhead is independent of the model dimension. We prove that the decoded encryption noise preserves the O(1/K)O(1/\sqrt{K}) convergence rate up to a negligible noise floor. The protocol is secure against an honest-but-curious server colluding with up to N1N-1 clients, and numerical results on MNIST validate the analysis.

Comments: 12 pages, 3 figures

Cite

@article{arxiv.2605.30123,
  title  = {Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channels},
  author = {Anthony Ayli and Khalil Harris and Jihad Fahs and Mohamad Assaad},
  journal= {arXiv preprint arXiv:2605.30123},
  year   = {2026}
}