Advancing Medical Recommendations With Federated Learning on Decentralized Data: A Roadmap for Implementation

This proposal presents a road-map for implementing federated learning (FL) for personalized medical recommendations on decentralized data. FL is a privacy-preserving technique allowing multiple parties to train machine learning models collaboratively without sharing their data. Our proposed framework incorporates differential privacy techniques to protect patient privacy. We discuss several evaluation metrics, including KL divergence, fairness, confidence intervals, top-N hit rate, sensitivity analysis, and novelty to evaluate the performance of the federated learning system. These metrics collectively serve as a robust toolbox for assessing Space needed the performance of the federated learning system. The proposed framework and evaluation metrics can provide valuable insights into the system's effectiveness and guide the selection of optimal hyperparameters and model architectures.