Federated Learning for Privacy-Preserving Healthcare Data Analysis in the Age of Cybersecurity Threats

This examination explores joined picking up gathering appraisals, unequivocally United Averaging (FedAvg), Weighted Consolidated Averaging (FedAvg-W), Bound together Learning with Adaptable Learning Rate (FedAdapt), and Secure Combination for Brought Learning (SecAgg), inside the space of assertion saving clinical benefits data assessment. The reason for the organized assessments was to assess their performance in terms of accuracy, evidence coverage and communication speed. This article provides a comparative evaluation to help practitioners select the most appropriate algorithm for clinical reasoning applications. The results show that FedAvg-W achieves much higher accuracy than other algorithms especially when used in locations with varying data attributes implying that it can adapt to the changes. In relation to this, a method called FedAdapt mixes quickly while maintaining high accuracy by way of dynamically changing learning rate limits with respect to particular instances of distribution information. A secure aggregation framework based on homomorphic encryption guarantees exact compliance. The review provides subtle experiences into space-related works, such as health informatics and federated learning. On one hand, SecAgg fulfills a basic requirement for ensuring and preserving medical benefits data while on the other side, FedAdapt's flexibility concerns the anticipated scalability of clinical evaluation applications.