PPBEFL: PRIVACY-PRESERVING BLOCKCHAIN ENABLED FEDERATED LEARNING FOR HEALTHCARE DATA SECURITY

Blockchain (BC)-based platforms have emerged as a result of the technology's explosive expansion., each with distinct structures and consensus mechanisms. This has heightened the focus on blockchain interoperability, facilitating interactions between different platforms. In decentralized learning environments, maintaining security, transparency, and trust is particularly challenging, particularly in delicate sectors like financial services and healthcare, where data centralization is not a viable option. The Blockchain-Enabled Federated Learning (BEFL) approach, which combines blockchain technology with this work, federated learning (FL) is provided as a dependable and effective alternative. By using an aggregation technique to eliminate anomalous model parameters and integrating blockchain procedures and privacy strategies to synchronize client privacy protection, PPBEFL protects against poisoning attempts. The PPBEFL model trains the client’s datasets using local models of the Visual Geometry Group 19 (VGG19) for images and a Convolutional Neural Network (CNN) for the dataset. The updates are serialized to avoid concurrency issues and recorded immutably on the blockchain. Aggregated updates refine a global model iteratively in a transparent and verifiable way. Heart Disease (HD) and Breast Cancer (BC) Image Datasets (Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM)) are used for experiments, Brain Tumor Magnetic Resonance Imaging (MRI) Dataset, and Breast Cancer Wisconsin (Diagnostic) comparison with typical FL schemes like FedAvg, Federated Learning with Multi-Party Computation (FL-MPC), Federated Learning with Robust Aggregation in Edge Computing (FL-RAEC), and the privacy-preserving and efficient FL (PEFL) all show improved defense against different types of attacks based on metrics like throughput, precision, loss, and delay.