Addressing Privacy-Preservation in Healthcare Using Federated Learning: A Survey

Abstract

The healthcare data are rapidly increasing, and protecting patient-sensitive information becomes challenging. This paper surveys the use of federated learning (FL) to address privacy in the healthcare industry. FL is a decentralized machine learning (ML) approach, where the ML process is distributed across multiple devices, without relying on a central server or coordinator. In recent years, FL has obtained significant attention, especially in scenarios where data privacy is top concern. This work attempts to discover the progress made so far regarding protecting healthcare information. This work explores the privacy risks related to a centralized healthcare system and also discusses the FL conceptual framework, which addresses many concerns. An extensive survey of various FL architectures and protocols designed for healthcare environments is conducted. It also investigates novel ways to deploy FL approaches using advanced encryption methods, like homomorphic and secure multi-party encryption, to improve privacy concerns. Moreover, this work covers the practical limitations and challenges of FL in the realm of healthcare, which include communication costs, model aggregation techniques, and scaling concerns. It also highlights new trends and directions in this field. Finally, the study discusses clinical applications of FL, namely personalized medicine, predictive analytics, or scaling issues.

Received: 29 July 2024 | Revised: 28 November 2024 | Accepted: 18 March 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Faria Karamat: Conceptualization, Data curation, Writing – original draft, Conceptualization. Atta Ur Rahman: Investigation, Resources, Supervision. Bibi Saqia: Methodology, Writing – review & editing, Visualization. Adeel Zafar: Validation, Project administration. Waqas Ali Khan: Formal analysis.