FedQFS: A Blockchain-Based Federated Learning Framework Based on Quality Auditing, Fairness Deviation, and Sybil-Resistant Similarity

Abstract

Federated Learning (FL) integrated with blockchain technology effectively mitigates the risks of single points of failure and malicious server behavior inherent in traditional federated learning architectures that rely on central servers. However, in practical implementation, the system still faces significant challenges in ensuring security and fairness. Malicious clients may upload low-quality or biased local models to disrupt global convergence, while Sybil nodes can forge multiple identities to manipulate aggregation, leading to severe performance degradation. To address these issues, this paper pro-poses a blockchain-based federated learning framework based on quality auditing, fairness deviation, and Sybil-resistant similarity (FedQFS). First, a quality auditing mechanism is designed to evaluate and filter local model updates through multi-dimensional metrics, effectively mitigating global ac-curacy degradation caused by malicious updates. Second, a Sybil-resilient identification mechanism is introduced, which leverages parameter similarity analysis to accurately detect forged identities, thereby enhancing the system's resistance against Sybil attacks. Finally, a fairness deviation quantification mechanism is incorporated to measure parameter distribution disparities and adaptively assign reasonable aggregation weights to benign clients with limited data, ensuring fairness in global model updates. Experimental results show that the proposed framework achieves over 95.5% accuracy on the MNIST dataset and maintains strong robustness under Sybil attacks scenarios. When four label-flipping attackers are present, its attack success rate de-creases by 18.4% compared with mainstream aggregation algorithms, validating the proposed method’s efficiency and security in complex distributed environments.