Privacy-Preserving Federated Learning Framework for Cross-Border Biomedical Data Governance: A Value Chain Optimization Approach in CRO/CDMO Collaboration

Abstract

This paper presents a novel privacy-preserving federated learning framework for cross-border biomedical data governance in CRO/CDMO collaborations. The proposed framework integrates edge intelligence with differential privacy mechanisms to address the challenges of secure data sharing while optimizing value chain performance. The architecture implements a three-fold hierarchical structure: edge-based data processing, federated model training, and global parameter aggregation. A comprehensive privacy protection mechanism utilizing artificial noise functions and theoretical convergence bounds ensures data security while maintaining model utility. Experimental validation across four major datasets demonstrates the framework's effectiveness, achieving 92.8% model accuracy while reducing the privacy budget by 80% compared to traditional approaches. The implementation results show a 62.5% reduction in training time and 68.3% decrease in communication costs. Value chain optimization analysis reveals a 45% operational cost reduction and a 65% improvement in data utilization efficiency. The framework establishes a robust foundation for secure cross-border biomedical data collaboration while ensuring regulatory compliance and operational efficiency.