A Proactive Defense Framework for Federated Learning Unifying Post-Quantum Security and Byzantine Resilience

Abstract

Federated Learning (FL) is emerging as a pivotal paradigm for privacy-preserving artificial intelligence, yet its distributed architecture introduces a complex threat landscape encompassing both contemporary and forward-looking vulnerabilities. To address these challenges, this paper presents a proactive defense framework designed to establish end-to-end trust through a holistic security posture. Our architecture is founded upon a dual-layered defense mechanism, a forwardsecure communication channel hardened with NIST-standardized Post-Quantum Cryptography (PQC) algorithms, and a dynamic, multi-criteria resilience protocol engineered to neutralize presentday Byzantine attacks. This protocol synthesizes insights from behavioral gradient analysis and public data validation into a robust reputation system that progressively isolates malicious participants. We empirically validate the framework's efficacy through two distinct methodologies. First, a rigorous stresstest on a 10-client network with malicious populations ranging from 10% to 40% demonstrates high performance, achieving 92.98% accuracy under a 10% attack. Second, to assess realworld viability, we conduct a large-scale scalability test on a 100client network. In this more challenging scenario, the framework demonstrates remarkable resilience, containing a 10% attack and achieving a robust final accuracy of 77.12%. These experiments reveal a key tunable trade-off between maximal performance under heavy assault and learning stability. Significantly, the PQC security layer imposes a fixed and predictable communication overhead, confirming its practicality. This work presents a validated architectural blueprint for a robust and scalable security framework for high-stakes FL deployments.