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With the increasing importance of data sharing for collaboration andinnovation, it is becoming more important to ensure that data is managed andshared in a secure and trustworthy manner. Data governance is a common approachto managing data, but it faces many challenges such as data silos, dataconsistency, privacy, security, and access control. To address thesechallenges, this paper proposes a comprehensive framework that integrates datatrust in federated learning with InterPlanetary File System, blockchain, andsmart contracts to facilitate secure and mutually beneficial data sharing whileproviding incentives, access control mechanisms, and penalizing any dishonestbehavior. The experimental results demonstrate that the proposed model iseffective in improving the accuracy of federated learning models while ensuringthe security and fairness of the data-sharing process. The research paper alsopresents a decentralized federated learning platform that successfully traineda CNN model on the MNIST dataset using blockchain technology. The platformenables multiple workers to train the model simultaneously while maintainingdata privacy and security. The decentralized architecture and use of blockchaintechnology allow for efficient communication and coordination between workers.This platform has the potential to facilitate decentralized machine learningand support privacy-preserving collaboration in various domains.

Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior