Privacy-Preserving Federated Learning in Healthcare: Integrating Homomorphic Encryption, Smart Contracts, and Adaptive Strategies

Abstract

The exponential rise of healthcare data calls for strong privacy-preserving methods for collaborative learning. In this paper, an Integrated Federated Learning Framework integrating homomorphic encryption, blockchain, and adaptive learning approaches is introduced to meet the challenges of privacy, scalability, and heterogeneity of data in healthcare. Data confidentiality is provided by homomorphic encryption with the ability to carry out computations over encrypted data safely, and tamper-proof sharing of data by blockchain. Adaptive learning mechanisms such as weighted federated learning and SMOTE address data imbalance and heterogeneity. Evaluated on real-world data such as MIMIC-III, the framework achieves 93.2% model accuracy, outperforming traditional centralized and federated learning approaches. Experimental results are a 30% reduction in communication overhead with faster convergence, making it feasible for large-scale, privacy-sensitive healthcare applications. The proposed framework provides opportunities for secure, efficient, and collaborative analysis of healthcare data.