Post-quantum Federated Learning: Secure And Scalable Threat Intelligence For Collaborative Cyber Defense

TL;DR

This paper introduces a quantum-secure federated learning framework using post-quantum cryptography to enhance the security and scalability of collaborative cyber threat intelligence sharing, addressing quantum attack vulnerabilities.

Contribution

It proposes a hybrid architecture integrating NIST-standardized post-quantum algorithms for secure, scalable federated learning in threat intelligence applications.

Findings

Achieved 97.6% threat detection accuracy on APT datasets

Demonstrated minimal latency overhead of 18.7%

Validated secure ransomware indicator sharing in healthcare case study

Abstract

Collaborative threat intelligence via federated learning (FL) faces critical risks from quantum computing, which can compromise classical encryption methods. This study proposes a quantum-secure FL framework using post-quantum cryptography (PQC) to protect cross-organizational data sharing. We expose vulnerabilities in traditional FL through simulated quantum attacks on RSA encrypted gradients and introduce a hybrid architecture integrating NIST-standardized algorithms CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication. Testing on APT attack datasets demonstrated 97.6% threat detection accuracy with minimal latency overhead (18.7%), validating real-world viability. A healthcare consortium case study confirmed secure ransomware indicator sharing without breaching privacy regulations. The work highlights the urgency of quantum ready defenses and provides technical guidelines for deploying PQC in FL systems, alongside policy recommendations for standardizing quantum resilience in threat-sharing networks.