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New Protocol Enhances Federated Learning Privacy with Multi-Key Encryption

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel four-phase protocol for privacy-enhanced federated learning (FL) that utilizes the xMK-CKKS multi-key homomorphic encryption scheme over wireless channels. This protocol enables secure aggregation of encrypted data without requiring channel estimation, addressing vulnerabilities in single-key HE methods. The proposed system integrates with zero-order FL, ensuring security against a server colluding with up to N-1 clients and maintaining an O(1/sqrt(K)) convergence rate, as validated by numerical results on MNIST. AI

IMPACT This research could lead to more secure and private federated learning systems, crucial for sensitive data applications.

RANK_REASON The cluster contains a research paper detailing a new protocol for federated learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New Protocol Enhances Federated Learning Privacy with Multi-Key Encryption

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Anthony Ayli, Khalil Harris, Jihad Fahs, Mohamad Assaad ·

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

    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 sc…

  2. arXiv cs.LG TIER_1 English(EN) · Mohamad Assaad ·

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

    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-equali…

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