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*Federated learning* is a technique that allows multiple parties to train a machine learning model collaboratively without sharing their data. This paper explores the use of federated learning in healthcare, where privacy is paramount. It shows how federated learning can be used to improve the accuracy of *medical diagnoses* while preserving patient privacy. This paper is a game-changer for anyone working with sensitive healthcare data, as it introduces a method to train high-accuracy diagnostic models without compromising patient privacy. By enabling multiple healthcare providers to collaborate on model training without sharing their data directly, federated learning opens new avenues for medical research and improved patient outcomes. The paper not only addresses the technical aspects of implementing federated learning but also delves into the ethical considerations, ensuring that the technology is used responsibly. With the increasing demand for AI-driven healthcare solutions, this research offers a practical and secure pathway for leveraging the power of machine learning to enhance medical diagnoses and treatment strategies.
