Cross‐Border Healthcare Data Sharing Using Blockchain and Federated Learning

Abstract

Cross-border healthcare data sharing is critical to advance precision treatment, rare illness research, and global public health surveillance. However, legislative limits, privacy concerns, and institutional mistrust severely impede centralized data pooling across nations. Federated learning (FL) has emerged as a potential paradigm that allows for collaborative model training without needing raw data transmission, while blockchain technology provides decentralized trust, auditability, and governance mechanisms. In recent years, the convergence of blockchain and federated learning has gained substantial attention as a solution for secure, privacy-preserving, and accountable cross-border healthcare analytics. In this chapter, we have systematically analyzed architectural designs, privacy and security mechanisms, incentive and governance models, and optimization strategies adopted in recent research on blockchain-enabled federated learning for cross-border healthcare data sharing, We further compare datasets, experimental setups, evaluation metrics, and empirical findings across studies to highlight strengths and limitations of current approaches in key application domains include medical imaging, electronic health records, genomic analysis, Internet of Medical Things (IoMT), and pandemic response systems. Finally, we outline promising future research directions toward standardized benchmarks, policy-aware learning orchestration, and deployable cross-border healthcare intelligence systems.