Empowering Industrial Federated Learning: Blockchain-Based Model Validation and Incentive Mechanism

In modern industry, industrial federated learning (IFL) has emerged as a promising framework for enabling privacy-preserving machine learning collaboration among factories to eliminate data silos. However, in the IFL setting, the central server may maliciously manipulate the global model by favoring specific clients or launching poisoning attacks. Similarly, malicious clients may degrade the model performance or exploit the system through free-riding, undermining fairness and stability. This article, for the first time, proposes a Blockchain-based Validation and Incentive Mechanism (BVIM) to address these critical challenges. BVIM innovatively integrates blockchain technology with the IFL framework: on the one hand, it removes the dependence on a central server through decentralization, ensuring transparency and traceability in the collaboration process; on the other hand, it incorporates a model validation strategy into the IFL workflow, enabling robustness even when up to half of the participating clients are malicious. Moreover, BVIM penalizes clients engaged in poisoning or free-riding behaviors while rewarding honest participants who contribute valuable local data and computational resources, thus establishing a fair and efficient incentive mechanism. Extensive experiments on the Tennessee-Eastman process (TEP) dataset, non-independent and identically distributed (i.i.d.) TEP partitions, and Fashion-MNIST show that BVIM achieves robust model validation, stable global convergence, and contribution-aware incentives under different attack settings. We further compare BVIM with representative defense baselines and analyze the communication overhead introduced by blockchain operations. The results validate the design goals of BVIM and pave the way for the broader adoption of IFL in interfactory data sharing and collaborative machine learning, fostering a more secure, trustworthy, and cooperative industrial ecosystem.