Integrating blockchain with federated learning for distributed cloud data security

Abstract

The combination of blockchain technology with federated learning (FL) introduces an innovative method to improve security, privacy, and trust in decentralized machine learning systems. Federated learning allows for distributed model training while safeguarding data privacy by keeping original data on local devices. Nonetheless, it confronts issues such as ensuring data integrity, the reliability of model updates, and vulnerability to adversarial attacks. Blockchain technology creates an immutable, decentralized ledger that  guarantees transparency, secure aggregation, and verifiable updates to models. By utilizing blockchain's consensus protocols, smart contracts, and cryptographic methods, FL can counteract threats like poisoning attacks and eliminate single points of failure. This paper examines the architectural framework, advantages, and challenges of merging blockchain with FL, in addition to potential enhancements to boost scalability and efficiency. We also emphasize practical applications and prospective research pathways in this evolving field.