Federated Learning for Scalable Big Data Analytics in Healthcare and Edge Systems

Abstract

Federated learning (FL) has emerged as a transformative paradigm for scalable big data analytics, particularly in privacy-sensitive sectors like healthcare and edge computing systems. By enabling collaborative machine learning across decentralized devices without the need to transfer raw data, federated learning preserves data privacy while unlocking the potential of big data. This paper explores the application of federated learning to healthcare and edge computing environments, focusing on its ability to handle large-scale, distributed data while ensuring compliance with stringent privacy regulations, such as HIPAA and GDPR. Through case studies and experiments, we demonstrate how federated learning can be effectively integrated into healthcare systems for predictive analytics, personalized medicine, and real-time monitoring of patient data. Furthermore, we highlight the advantages of federated learning in edge computing systems, including energy efficiency, low latency, and the ability to process data locally. The study examines the performance of various federated learning algorithms, including FedAvg and FedProx, in terms of accuracy, communication cost, and resource consumption. The results indicate that federated learning can provide a scalable and privacy-preserving solution for big data analytics in both healthcare and edge computing, overcoming the limitations of traditional centralized data processing approaches. Finally, we propose future research directions to enhance the scalability, efficiency, and security of federated learning models in healthcare and edge ecosystems.