Kumari, R.Sah, D.K.Gupta, S.Cengiz, K.Ivkovic, N.2024-05-192024-05-1920230098-3063https://doi.org/10.1109/TCE.2023.3334159https://hdl.handle.net/20.500.12713/4337This proposal presents a road-map for implementing federated learning for personalized medical recommendations on decentralized data. Federated learning 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 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. Our framework incorporates differential privacy methods to safeguard patient information effectively. IEEEeninfo:eu-repo/semantics/closedAccessData ModelsData PrivacyDecentralized DataDistributed DatabasesFederated LearningFederated LearningMedical Diagnostic İmagingMedical ServicesModel ArchitecturePersonalized Medical RecommendationsSensitivity AnalysisTrainingArticle112-s2.0-8517909645310.1109/TCE.2023.3334159Q1