Privacy-Enhanced Zero-Order Federated Learning Using Multi-Key Homomorphic Encryption Over Wireless Channels
Researchers have proposed a four-phase protocol combining extended multi-key CKKS homomorphic encryption (xMK-CKKS) with zero-order federated learning to enable secure model aggregation over wireless channels without requiring channel estimation. The approach addresses a known vulnerability in existing methods where clients sharing a single secret key could potentially access each other's private updates. The work is significant because it offers stronger client-level privacy guarantees while keeping communication and encryption overhead independent of model size.
A preprint posted to arXiv introduces a protocol that integrates xMK-CKKS homomorphic encryption with zero-order federated learning (FL) to allow a non-trusted server to aggregate encrypted model updates transmitted over a shared wireless channel. Unlike prior homomorphic-encryption-over-the-air (OTA) approaches that rely on single-key schemes and require channel estimation or pre-equalization to handle wireless fading, the new protocol retransmits partial public keys and ciphertexts through the same channel realization so that dominant large-modulus encryption terms cancel algebraically during decryption. Each participating device transmits only a single encrypted scalar per communication round, making the overhead independent of the underlying model's dimensionality. The protocol is designed to be secure against both a non-trusted server and honest-but-curious (HBC) clients, preventing any participant from recovering another's local updates. Theoretical analysis shows the method preserves the standard O(1/√K) convergence rate up to a negligible noise floor, and numerical experiments on the MNIST dataset validate these findings.
What's missing
The study evaluates the protocol only on the relatively simple MNIST benchmark; performance on larger, more complex models and real-world heterogeneous wireless environments (e.g., non-LoS or rapidly fading channels) remains untested. The paper assumes slowly varying line-of-sight (LoS)-dominant channels, which may limit applicability in practical mobile deployments. Scalability with respect to a large number of participating clients and the computational cost of xMK-CKKS on resource-constrained edge devices are not fully characterized.
What different sources said
- arXiv cs.LGCenter
Privacy-Enhanced Zero-Order Federated Learning via xMK-CKKS over Wireless Channels
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