A Communication-Efficient Federated Learning Framework FOR Privacy-Preserving Disease Diagnosis IN Low-Resource Healthcare Systems
Keywords:
Federated Learning; Privacy-Preserving AI; Medical Diagnosis; Healthcare Informatics; Low-Resource Healthcare; Artificial Intelligence in HealthcareAbstract
However, in low-resource countries, implementing A.I.-driven diagnostic systems is a daunting task because of privacy issues, restrictions in computational resources and network reliability. Current centralized machine learning approaches involve sharing of sensitive patient information with external servers, which raises privacy concerns and restricts collaborative healthcare analytics. In this paper, the authors present a communication efficient federated learning model for privacy-preserving disease detection in resource-limited healthcare systems. It combines lightweight convolutional neural networks with secure parameter aggregation to minimize the amount of communication needed without compromising diagnostic performance. We have performed experiments with the Chest X-ray pneumonia dataset distributed to simulated healthcare clients. The suggested model was found to be accurate in diagnostics with a value of 94.1% and the communication cost was reduced by 36% from the traditional federated learning model. The study presents a privacy-preserving and scalable framework for health care AI that is appropriate for decentralized medical diagnosis in remote areas.
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