arXiv:2605.30123v1 Announce Type: cross 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
The increasing adoption of federated learning in privacy-sensitive applications and the recognized vulnerabilities of current homomorphic encryption methods necessitate more robust solutions. This research emerges as a direct response to these evolving security and privacy demands.
This development allows for more secure and privacy-enhanced federated learning deployments, crucial for industries handling sensitive data and operating over potentially insecure wireless channels without compromising individual data privacy or system integrity. It addresses a fundamental security challenge in distributed AI.
The ability to perform federated learning with multi-key homomorphic encryption over wireless channels significantly improves the privacy and security posture of distributed AI systems, reducing the risk of data breaches from compromised clients and enabling broader adoption in sensitive domains.
- · AI developers
- · Privacy-focused industries (healthcare, finance)
- · Federated learning platforms
- · Cybersecurity firms
- · Attackers targeting FL systems
- · Single-key HE solution providers (if they don't adapt)
Increased adoption of privacy-preserving machine learning in sensitive applications becomes feasible and secure.
New standards and regulations may emerge for secure federated learning, potentially fostering a 'privacy-by-design' approach in AI development.
Enhanced trust in AI systems could accelerate the deployment of autonomous AI agents in highly regulated or critical infrastructure environments.
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