Identification of Kidney Disorders in Decentralized Healthcare Systems through Federated Transfer Learning

Abstract

This research introduces a pioneering approach to address the intricate challenge of identifying kidney abnormalities in medical imaging. By synergizing the strengths of transfer learning and federated learning (FL), the study propels the evolution of diagnostic capabilities within decentralized healthcare systems. Within the realm of patient data privacy, the federated transfer learning architecture operates harmoniously, collectively learning from geographically dispersed renal imaging datasets. This collaborative strategy empowers healthcare providers to unite their efforts while retaining ownership of their individual databases. This innovative methodology holds immense promise for augmenting the precision and efficacy of renal abnormality detection in medical imaging. Leveraging the wealth of knowledge embedded in transfer learning models, the approach adapts and refines these insights for the specific nuances of renal imaging data. This adaptive learning process occurs without compromising patient confidentiality and fortifies data security. This convergence of deep learning, federated learning, and transfer learning represents a transformative leap in healthcare. By facilitating a more efficient, compassionate, and decentralized approach to managing kidney-related health issues, this study charts a compelling path toward the future of medical image analysis. In doing so, it not only addresses the current challenges in identification of renal abnormality but also paves the way for a new era of collaborative and privacy-preserving healthcare solutions in decentralized healthcare systems.