Impact of Federated Learning on Personalized Healthcare Outcomes: The Mediating Role of Patient Data Privacy Preservation

Abstract

Federated Learning (FL) affects personalized healthcare outcomes by serving as a data privacy protection approach according to this study. Current healthcare providers adopt Federated Learning to perform distributed data training across multiple sources without sharing protected patient information between the sources. The FL approach provides an exceptional method to combine digital health technologies in healthcare delivery with protection for patient data privacy. The research investigates healthcare professionals in Jordan since these healthcare providers operate under stringent privacy regulations that justify this study design.The research reveals Federated Learning drives substantial improvements to individual healthcare results because patient data privacy stands as a critical element that establishes trust and advances FL technology use in healthcare facilities. The findings from this study demonstrate how FL functions as an essential force for healthcare transformation through privacy protection measures which help expand FL usage in worldwide healthcare platforms. Healthcare providers in Jordan and comparable regions should use Federated Learning to improve individualized healthcare services through privacy-protected patient information.

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