Federated Learning in Healthcare: Privacy Meets Fraud Detection
Author(s): Puneet Sharma
Publication #: 2412119
Date of Publication: 09.06.2023
Country: USA
Pages: 1-5
Published In: Volume 9 Issue 3 June-2023
DOI: https://doi.org/10.5281/zenodo.14566260
Abstract
The intersection of artificial intelligence (AI) and healthcare has opened transformative avenues for addressing critical challenges like fraud detection, patient privacy, and operational inefficiencies. Federated Learning (FL), an emerging subfield of machine learning, has gained traction as a solution that harmonizes privacy preservation with collaborative data utilization. By enabling decentralized model training across institutions without sharing raw data, FL mitigates privacy concerns while enhancing fraud detection capabilities.
This paper delves into the role of FL in healthcare, emphasizing its applications in fraud detection, patient data protection, and collaborative research. It also examines the challenges of implementing FL, including computational overhead and regulatory compliance, while exploring how advancements in cryptography and edge computing address these issues. The integration of FL with complementary technologies like blockchain and differential privacy further enhances its utility in healthcare. With its promise to balance privacy and innovation, FL is poised to redefine trust and efficiency in the healthcare landscape.
Keywords: Federated Learning, Healthcare Privacy, Fraud Detection, Decentralized AI, Differential Privacy, Blockchain Integration, Edge Computing, Collaborative Data Utilization, Secure AI Models, Privacy-Preserving Technologies
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