Enhancing Antiplagiarism Measures in Blockchain-Based Decentralized Federated Learning for Cross-Enterprise Modeling

Decentralized federated learning (DFL) has the potential to address the issue of the aggregator’s single-point failure. However, in the absence of centralized coordination, DFL systems are vulnerable to malicious behaviors from clients. In this article, we propose a blockchain-based DFL framework to regulate the behaviors of enterprise clients in the context of cross-enterprise modeling. To be specific, we first design a novel mechanism for model plagiarism detection, wherein pseudonoise sequences are incorporated into local models, enabling to identify enterprises’ plagiarism behaviors. Then, we propose a model aggregation algorithm to improve the learning performance of the global model. Furthermore, we develop a plagiarism-aware proof-of-work consensus mechanism by adaptively adjusting enterprises’ mining difficulty based on their plagiarism records, which can efficiently demotivate them from engaging in plagiarism. The experimental results based on industrial datasets, including CWRU, PU, Milan, PV, NEU-CLS, and X-SDD, demonstrate that the proposed framework can achieve approximately 4%, 7%, and 12% of the learning accuracy improvement in the scenarios of 20%, 40%, and 60% plagiarism rates, respectively, compared to the conventional DFL system.