Federated Learning for Privacy-Preserving Data Analytics in Healthcare
Rishabh Agrawal, Himanshu Kumar · Iconic Research and Engineering Journals · 2024
The rapid digitization of healthcare has resulted in unprecedented levels of sensitive patient data being generated from electronic health records, imaging, wearables, and genomics. Even though this flood of data offers immense opportunity for advanced analytics and predictive modeling, traditional centralized machine learning approaches present daunting privacy, security, and regulatory challenges. Federated Learning (FL) is a paradigm-breaking technique that enables the joint training of models across numerous institutions without the need to transfer raw data to a common repository. In this paper, the theory foundations, implementation frameworks, and real-world implications of FL are described within the context of privacy-preserving healthcare analytics. We explain how FL leverages decentralized model updates, secure aggregation, differential privacy, and homomorphic encryption to mitigate threats such as data leakage, model inversion, and adversarial attacks. Through recent case studies, the paper demonstrates how FL has been employed in clinical decision support, medical imaging, and wearable health monitoring with satisfactory performance comparable to centralized models. We also introduce the unique challenges of FL deployment in healthcare, including data heterogeneity, communication bottlenecks, and privacy-utility trade-offs. We conclude by noting emerging trends like integration with blockchain, edge computing, and new cryptographic techniques that can further enhance the resilience and scalability of federated healthcare systems. By offering a well-balanced overview of the opportunities and challenges in this field, the paper offers practical recommendations for policymakers, researchers, practitioners, and data analysts that can balance innovation and privacy protection when dealing with data-driven healthcare.