Title: Artificial Intelligence Across Disciplines: Research, Innovation, and Intelligent Solutions
Editors: Dr. Subita Bhagat, Dr. A. Balamurugan, Dr. P. Krishna Kumar, and Mrs. S. Nandhini Devi
ISBN: 978-81-69857-83-3
Chapter: 5
DOI: https://doi.org/10.59646/815/05
Author: Dr. K.P. Kaliyamurthie
Abstract
Cross-silo collaborative intelligence often encounters severe regulatory constraints under legislation such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), alongside catastrophic vulnerabilities stemming from single-point aggregation failures, poisoning attacks, and gradient leakage. This chapter presents a blockchain-enabled federated deep learning (BFDL) architecture tailored for decentralized, privacy-preserving multi-center medical diagnostics. By substituting fragile centralized parameter servers with an immutable, smart contract-governed consortium blockchain running practical Byzantine fault tolerance (PBFT), the framework achieves fully auditable, tamper-resistant parameter synchronization. Local deep convolutional neural networks train strictly within institutional perimeters. Weight updates undergo bounded norm clipping and localized Gaussian differential privacy noise infusion prior to submission. A specialized validation smart contract evaluates incoming model parameters via proof-of-gradient quality before recording global aggregation on the ledger. Comprehensive validation on a multi-institutional diabetic retinopathy detection case study demonstrates 95.34% global classification accuracy, near-lossless convergence, and robust Byzantine resilience.
Keywords: Blockchain, Federated Deep Learning, Differential Privacy, Smart Contracts, Healthcare Analytics, Privacy-Preserving AI.