Decentralized Data Governance and Regulatory Compliance in Federated Learning and Edge Computing for Healthcare

The paper examines the integration of decentralized data governance and regulatory compliance in the framework of federated learning and edge computing for healthcare. The cumulative reliance on digital technologies in healthcare enforces strong frameworks that ensure data privacy, security, and regulatory adherence. Federated learning, which allows machine learning (ML) models to be trained across multiple decentralized devices without sharing raw data, and edge computing, which processes data near its source, tender hopeful resolutions. The study explores into the ideologies of decentralized data governance, highlighting its benefits in maintaining data locality, enhancing privacy, and improving security. By examining many privacy-preserving techniques i.e. differential privacy and homomorphic encryption, the study exemplifies how these methods can be effectively implemented within federated learning and edge computing frameworks. Moreover, the study addresses the critical aspect of regulatory compliance, focusing on key regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Policies for ensuring compliance, including data encryption, access controls, and audit trails, are carefully studied. Through case studies and practical implementations, the paper demonstrates the feasibility and advantages of combining decentralized data governance with federated learning and edge computing.