Zero-Knowledge Federated Learning with Lattice-Based Hybrid Encryption for Quantum-Resilient Medical AI

Researchers introduce ZKFL-PQ, a quantum-resistant cryptographic protocol for federated learning in medical AI that combines zero-knowledge proofs, lattice-based encryption, and homomorphic encryption. The protocol achieves 100% rejection of malicious updates while maintaining model accuracy, addressing vulnerabilities from gradient inversion attacks and future quantum threats.

• →ZKFL-PQ protocol hybridizes ML-KEM, lattice-based zero-knowledge proofs, and BFV homomorphic encryption for quantum-resistant federated learning.
• →The system achieved 100% rejection of norm-violating malicious updates while maintaining full model accuracy in medical imaging tests.
• →Protocol addresses the 'Harvest Now, Decrypt Later' quantum threat by using post-quantum cryptographic methods.
• →Computational overhead is approximately 20x but remains compatible with clinical research workflows on daily/weekly cycles.
• →Current defense only protects against large-norm attacks, with subtle poisoning attacks remaining an open research problem.