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

2026-03-08

AI Summary

This work proposes the first quantum-resistant federated learning framework for threat intelligence sharing, integrating the NIST-standardized post-quantum algorithms CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures. Addressing the vulnerability of traditional federated learning to quantum attacks and the inadequacy of classical encryption in safeguarding gradient updates, the framework ensures quantum-safe key exchange, robust authentication, and privacy-compliant data sharing. Experimental evaluation demonstrates a 97.6% threat detection accuracy on the APT dataset with only an 18.7% communication overhead. Furthermore, the approach successfully enables secure sharing of ransomware indicators in a healthcare consortium setting, offering both a technical foundation and policy guidance for building quantum-resilient threat intelligence networks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Game Theory and Economic Paradigms: Adversarial Learning
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Applications of cryptography
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

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.

Problem

• post-quantum cryptography
• federated learning
• quantum attacks
• threat intelligence
• cyber defense

Innovation

• Post-quantum cryptography
• Federated learning
• CRYSTALS-Kyber
• CRYSTALS-Dilithium
• Quantum-secure framework