Privacy-Preserving AI Through Blockchain and Federated Learning
Abstract
Artificial intelligence (AI) systems rely more on vast volumes of sensitive and personal data, ensuring that data privacy becomes crucial. Conventional centralized AI training techniques fail to comply with stringent legal standards and pose privacy hazards. This chapter introduces a novel privacy protection system that combines federated learning (FL) and blockchain technology to facilitate the use of decentralized, secure, and legally compliant AI. FL removes the requirement to distribute raw data by enabling model training directly on user devices or institutional data silos. FL, however, presents new difficulties about openness, trust, and the accuracy of model updates. Blockchain serves as a decentralized trust layer by providing verifiable update tracking, immutable logging, and the capacity to impose data governance standards through smart contracts. The proposed architecture provides a robust foundation for data protection, ensuring model performance is not compromised by incorporating additional privacy-enhancing technologies, including homomorphic encryption, secure multiparty computation, and differential privacy. This chapter represents one of the best-structured and most comprehensive studies that combines the concepts of FL, blockchain, and privacy-preserving mechanisms into a single chapter, thus contributing to research and acting as an educational reference for future research work.
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