Advancing Medical Recommendations With Federated Learning on Decentralized Data: A Roadmap for Implementation
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This 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. IEEE
Açıklama
Anahtar Kelimeler
Data Models, Data Privacy, Decentralized Data, Distributed Databases, Federated Learning, Federated Learning, Medical Diagnostic İmaging, Medical Services, Model Architecture, Personalized Medical Recommendations, Sensitivity Analysis, Training
Kaynak
IEEE Transactions on Consumer Electronics
WoS Q Değeri
Scopus Q Değeri
Q1
