Blockchain based Homomorphic Encryption in Federated Learning for Alzheimer Detection

Alzheimer disease is a progressive neuro degenerative condition that slowly damages the brain and affects memory and thinking. Early treatment is important to slow its progression and improve the patient's quality of life. Currently millions of people are suffering from Alzheimer and the number is increasing rapidly. Recent advances in centralized machine learning with CNN sharpened accuracy by studying MRI patterns, but centralizing sensitive medical data raises privacy and security risks amid strict laws like GDPR and HIPPA.Federated learning provides decentralized approach for collaborative training of models without exposure to raw data. Federated Learning is widely used, but is still fragile, vulnerable to attacks, and lacking trust and robust mechanisms for secure aggregation. This paper presents a novel framework combining Blockchain with Federated Learning, Homomorphic Encryption and Serialization to enhance Alzheimer Detection. Leveraging Ethereum decentralized ledger and smart contracts, the framework ensures transparency, integrity, and resistance to malicious activities during the training process. The work computed the results using Simple Federated Averaging algorithm, Federated Averaging with Homomorphic Encryption and proposed framework. The work depicts that enhanced privacy, security, and scalability for an integrated approach can be derived from this framework. Thus, apart from protecting sensitive health information, it provides a transparent, decentralized, and distributed paradigm for carrying out of medical research leading to secure collaborative innovations in diagnostics and treatment methodology with future plans to test larger datasets and advanced encryption for broader real time healthcare applications across the globe.