Privacy-Preserving Data Analysis Using Federated Learning: A Practical Implementation Study

Abstract

Collaborative data analysis across organizations remains constrained by privacy preservation requirements, particularly within healthcare and financial sectors. This study develops a practical federated learning framework enabling multiple entities to jointly train machine learning models without raw data exposure. We implement differential privacy mechanisms within a distributed architecture, examining privacy-utility trade-offs through systematic experimentation. The proposed system integrates k-anonymity, l-diversity, and t-closeness techniques while maintaining computational efficiency. Performance evaluation demonstrates that our federated approach achieves 94.2% accuracy compared to centralized baselines while providing ε-differential privacy guarantees with ε=0.5. Communication overhead analysis reveals 73% reduction in data transmission compared to traditional collaborative methods. The framework successfully handles non-uniform data distributions across participants through adaptive aggregation protocols. Experimental validation on healthcare datasets shows 15.3% improvement in privacy preservation metrics while maintaining model convergence. Our implementation addresses practical deployment challenges including Byzantine robustness and dynamic participant management. The developed system provides organizations with actionable privacy-preserving analytics capabilities, supporting regulatory compliance while enabling valuable multi-party collaboration.