BTIMFL: A Blockchain-Based Trust Incentive Mechanism in Federated Learning

Federated learning (FL) is a machine learning technique that allows multiple devices to train a model collaboratively without sharing their data with a central server. It has advantages such as increased privacy, reduced communication costs, and improved scalability, making it useful in scenarios where data is distributed across multiple devices and privacy is a concern, such as in healthcare or finance. However, the potential for participants to behave selfishly in FL can be a challenge, and incentive mechanisms are needed to encourage them to participate in the training process. Incentives can take many strategies, such as financial rewards or reputation-based systems, and can be tailored to specific needs. In defense technology, FL can be used for predictive maintenance, target recognition, and intelligence analysis. However, the existing incentive mechanisms in FL have limitations, such as being complex to design and implement, and raising privacy concerns. To improve the incentive mechanisms in FL, we propose an incentive mechanism based on blockchain called BTIMFL that ensures transparency and effectiveness. The proposed mechanism includes DAO (Decentralized Autonomous Organizations) and smart contracts for the automatic distribution of profits to ensure fairness.