Enhancing Data Privacy in AI Systems Using Blockchain-Based Federated Learning

Abstract

The rise of data privacy concerns has led to the development of federated learning (FL), where AI models are trained across decentralized devices without centralizing sensitive data. However, federated learning still faces challenges related to data integrity and trust. This paper introduces a blockchain-based federated learning framework that ensures data privacy and integrity during model training. By employing a permissioned blockchain, we maintain a secure and immutable record of model updates, which are contributed by participating devices. Smart contracts are used to automate the verification of model updates, ensuring that only valid contributions are integrated into the global model. Our approach significantly reduces the risk of model corruption and data breaches while maintaining high levels of performance. Experimental evaluations across various datasets reveal that the proposed framework achieves comparable accuracy to traditional FL while providing enhanced security and privacy guarantees.