Blockchain-Assisted Federated Learning for Classification of Neurodegenerative Disorders Using Zero-Knowledge Proof

Abstract

Blockchain and Federated Learning (FL) provide a strong framework for distributed, privacypreserving machine learning in the medical field. In order to provide safe and effective model training, this framework assists in handling sensitive patient data from lung disease diagnosis, such as CT scans, X-rays, and clinical records. The proposed approach improves distributed machine learning security, privacy, and integrity, particularly in delicate fields like healthcare. Contributions from other datasets help the model get better, but patient data is kept private and blockchain guarantees the integrity of the updates to the model. ZeroKnowledge Proofs (ZKP) guarantee that customers can demonstrate the accuracy of their model upgrades without disclosing any personal information. FLBC- ZKP uses cryptographic proofs to remove this requirement for confidence. FLBC-ZKP models exhibit competitive accuracy rates in healthcare applications, guaranteeing confidentiality and privacy without compromising predictive performance. Contri- butions from other datasets improve the model, but patient information is kept confidential and the blockchain ensures the accuracy of model updates. Compared to regular FL, FLBC-ZKP delivers superior privacy and security through blockchain and ZKP, making it particularly suitable for sensitive healthcare data, while maintaining high accuracy. The accuracy data throughout federated learning rounds for a different approach, FLBC-ZKP slightly surpasses the other methods as the number of rounds increases.