Federated Learning for Secure Multi-State Medicaid Data Sharing and Analysis

Abstract

Given the varied architecture of Medicaid administration among U.S. states, attaching safe and effective data transmission is always challenging. Every state keeps its infrastructure, policies on privacy, and standards, which makes coordinated efforts across several jurisdictions difficult and comprehensive analytics impossible. This work explores how distributed machine learning approaches, federated learning, provide a sensible solution for secure and privacy-preserving analysis of Medicaid data over multiple states. Unlike pooling private data into a central repository, federated learning lets participating entities privately train shared models on their data while only giving model updates. This method dramatically lowers data leak risk and improves HIPAA and state-specific privacy regulations adherence. Maintaining patient confidentiality, the study investigates significant technological and organizational advantages like better predictive analytics, early fraud detection, and identification of population health trends. We demonstrate how federated learning enables cooperative insight in a multi-state environment with various data standards and infrastructure. The results reveal that federated learning advances trust and transparency among Medicaid agencies as well as scalable innovation in public health knowledge. At last, federated learning will enable states to address persistent problems with Medicaid data sharing, therefore enabling them to function efficiently while maintaining rigorous data privacy and security standards