Federated Learning in Healthcare: A Bibliometric Analysis of Privacy, Security, and Adversarial Threats (2021-2024)

Abstract

Federated Learning (FL) has rapidly emerged as a transformative machine learning approach, enabling healthcare institutions to collaboratively build predictive models without compromising patient data privacy. As healthcare increasingly adopts digital technologies, federated learning offers promising solutions to critical issues such as data privacy, security, data poisoning, and adversarial attacks. Despite the recognized potential of FL, significant gaps persist in existing research, particularly concerning comprehensive security frameworks and practical healthcare applications. This bibliometric analysis systematically explores the research landscape from 2021 to 2024, explicitly focusing on data privacy, security threats, and adversarial attacks within federated learning in healthcare. Utilizing bibliometric data from the Scopus database, the study identifies key thematic trends, evaluates global collaborative networks, and assesses contributions from leading institutions and countries. Findings reveal rapidly growing scholarly interest, robust international collaboration, and notable institutional contributions, with a specific emphasis on privacy-preserving techniques, healthcare-specific applications, and emerging technologies such as blockchain and edge computing. The analysis also highlights critical limitations due to incomplete bibliographic metadata. This research provides a comprehensive understanding of current trends and identifies future directions to enhance the security and privacy framework of federated learning in healthcare.