DMAFL: Effective defense against malicious attacker federated learning framework via blockchain and TFHE

A blockchain and threshold fully homomorphic encryption (TFHE)-based federated learning framework is proposed to defend against model poisoning and privacy leakage. Standard federated learning (FL) remains vulnerable to inference and poisoning attacks during model aggregation and distribution. To address these threats, we present a secure and efficient FL framework that combines TFHE with Shamir’s secret sharing for privacy-preserving encrypted updates. A multi-layer verification mechanism consisting of verifiable secret sharing (PVSS) and cosine similarity checks is introduced to identify and filter out malicious updates. Furthermore, we replace the central aggregator with a blockchain-based mechanism to eliminate single-point trust. Extensive experiments conducted on MNIST and CIFAR-10 under IID and Non-IID settings demonstrate that our scheme maintains high model accuracy under 20%–40% label-flipping attacks. Compared with baseline methods such as threshold Paillier and PPVFL, our approach reduces encryption and decryption overhead by up to 28.4%, while achieving a lower accuracy drop of only 2.32% on MNIST and 7.44% on CIFAR-10 under 40% poisoning. These results highlight the robustness, efficiency, and practicality of the proposed scheme for real-world federated learning deployments.