Tıpta Birleşik Öğrenme Yaklaşımı: Gizliliğin ve Model Kalitesinin Artırılması: Anlatısal Bir Derleme

Abstract

Birleşik Öğrenme (BÖ), ham verileri paylaşmadan birden fazla cihaz veya kurum arasında işbirlikçi model eğitimine olanak tanıyan ve böylece sağlık hizmetlerinde kritik gizlilik endişelerini gideren, merkezi olmayan bir makine öğrenimi paradigmasıdır. Bu derleme, BÖ'nün teknik ve etik zorlukları ele alırken hastalık tahminini, kişiselleştirilmiş bakımı ve sağlıkta eşitliği geliştirerek aile hekimliğini dönüştürme potansiyelini araştırıyor. BÖ'nün tıp alanındaki uygulamaları arasında hastalık tahmini, kişiselleştirilmiş tedavi, uzaktan hasta takibi ve kaynakların sınırlı olduğu ortamlarda sağlık eşitliğinin iyileştirilmesi yer almaktadır. BÖ, verdiği söze rağmen veri heterojenliği, hesaplama maliyetleri, etik kaygılar ve düzenleyici belirsizlik gibi zorluklarla karşı karşıyadır. Gelecekteki yönelimler arasında hibrit BÖ mimarileri, blok zinciri entegrasyonu, uç bilişim ve küresel sağlık girişimleri yer alıyor. Bu derleme, BÖ'nün aile hekimliği için dönüştürücü bir potansiyele sahip olduğu, hasta sonuçlarını iyileştirmek ve sağlık hizmeti eşitsizliklerini gidermek için gizliliği koruyan, veri odaklı çözümler sunduğu sonucuna varmıştır. Ancak başarısı, multidisipliner işbirliği ve hasta merkezli yönetim çerçeveleri aracılığıyla teknik, etik ve düzenleyici engellerin ele alınmasına bağlıdır.

Keywords

• Birleşik Öğrenme
• Gizliliğin Korunması
• Makine Öğrenimi
• Sağlık Bilişimi
• Kişiselleştirilmiş Tıp
• Sağlık Eşitliği

Federated Learning Approach in Medicine: Enhancing Privacy and Model Quality: A Narrative Review

Abstract

Federated Learning (FL) is a decentralized machine learning paradigm that enables collaborative model training across multiple devices or institutions without sharing raw data, thereby addressing critical privacy concerns in healthcare. This narrative review explores the potential of FL to transform family medicine by enhancing disease prediction, personalized care, and health equity while addressing technical and ethical challenges. FL's applications in medicine include disease prediction, personalized treatment, remote patient monitoring, and improving health equity in resource-limited settings. Despite its promise, FL faces challenges such as data heterogeneity, computational costs, ethical concerns, and regulatory ambiguity. Future directions include hybrid FL architectures, blockchain integration, edge computing, and global health initiatives. This review concludes that FL holds transformative potential for family medicine, offering privacy-preserving, data-driven solutions to improve patient outcomes and bridge healthcare disparities. However, its success depends on addressing technical, ethical, and regulatory barriers through multidisciplinary collaboration and patient-centric governance frameworks.

Keywords

• Federated Learning
• Privacy-Preserving
• Machine Learning
• Healthcare Informatics
• Personalized Medicine
• Health Equity

References

1. 1. Brauneck A., Schmalhorst L, Majdabadi MMK, Bakhtiari M, Völker U, Baumbach, J, Buchholtz G. Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review. Journal of Medical Internet Research. 2023;25:1-17.
2. 2. Crosson J, Stroebel C, Scott J, Stello B, Crabtree, B. Implementing an Electronic Medical Record in a Family Medicine Practice: Communication, Decision Making, and Conflict. The Annals of Family Medicine. 2005;3:307-11.
3. 3. Ali M, Naeem F, Tariq M, Kaddoum G. Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey. IEEE Journal of Biomedical and Health Informatics. 2022;27:778-89.
4. 4. Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, Maier-Hein K. The future of digital health with federated learning. NPJ digital medicine. 2020;3(1):1-7.
5. 5. Huang C, Huang J,Liu X. Cross-Silo Federated Learning: Challenges and Opportunities. 2022;ArXiv,abs/2206.12949.
6. 6. Loftus T, Ruppert M, Shickel B, Ozrazgat-Baslanti T, Balch J, Efron P, Bihorac A. Federated learning for preserving data privacy in collaborative healthcare research. Digital Health. 2022;1:8.
7. 7. Antunes RS, André da Costa C, Küderle A, Yari IA, Eskofier B. Federated learning for healthcare: Systematic review and architecture proposal. ACM Transactions on Intelligent Systems and Technology. (TIST) 2022;13(4):1-23. 8. Aouedi O, Sacco A, Piamrat K, Marchetto G. Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions. IEEE Journal of Biomedical and Health Informatics. 2022;27:790-803. 9. Hernandez J, Sadilek A, Liu L, Nguyen D, Kamruzzaman M, Rader B, Howell M. Privacy-first health research with federated learning. NPJ digital medicine. 2020;1:4.
8. 10. Wu X, Pei J, Chen C, Zhu Y, Wang J, Qian Q, Guo Y. Federated Active Learning for Multicenter Collaborative Disease Diagnosis. IEEE Trans Med imaging. 2022;42:2068-80.

9. 11. Tang G, Black J, Williamson T, Drew S. Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data. AMIA Annu Symp Proc. 2024 ArXiv,abs/2408.12029.
10. 12. Birari D. Towards a Holistic Approach to Chronic Disease Management: Integrating Federated Learning and IoT for Personalized health Care. Journal of Electrical Systems. 2024 13. Nagpal Y, Kukreja V, Singh DP, Vats S, Mehta S. Transformative Approaches: Diagnosing Lung Diseases through Federated Learning CNN. 2023 4th IEEE Global Conference for Advancement in Technology (GCAT). 2023;1:1-6.
11. 14. Mocanu I, Smadu RA, Drăgoi M., Mocanu A, Cramariuc O. Testing Federated Learning on Health and Wellbeing Data. 2021 International Conference on e-Health and Bioengineering (EHB). 2021;1:1-4.
12. 15. Ramanathan M, Sundaram P, Kumar S, Devi M. A Comprehensive Analysis of Personalized Medicine: Transforming Healthcare Privacy and Tailoring through Interoperability Standards and Federated Learning. 2024 Sixth International Conference on Computational Intelligence and Communication Technologies (CCICT). 2024;1:298-309.
13. 16. Li M , Xu P, Hu J, Tang Z, Yang G. From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare. Med Image Anal. 2025;101:1-25.
14. 17. Anavangot V, Lukose J. Calibration and Classifier Design for IoT Healthcare Applications using Federated Learning. 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS). 2022;1:1-6.
15. 18. Govardanan GC, Murugan R, Yenduri GG, Bhulakshmi D, K DR, Jhaveri R. The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review. Recent Advances in Computer Science and Communications. 2023;17(4):1023-41.
16. 19. Miranda R, Oliveira M, Nicola P, Baptista F M, Albuquerque I. Towards A Framework for Implementing Remote Patient Monitoring From an Integrated Care Perspective: A Scoping Review. International Journal of Health Policy and Management. 2023;12.
17. 20. Bhatia AS, Neira DEB. Federated learning with tensor networks: a quantum AI framework for healthcare. Machine Learning: Science and Technology. 2024;5(4):045035.
18. 21. Kanhegaonkar P, Prakash S. Federated learning in healthcare applications. In Data Fusion Techniques and Applications for Smart Healthcare. 2024;2:157-96.
19. 22. Igwama GT, Olaboye JA, Maha CC, Ajegbile MD, Abdul S. Integrating electronic health records systems across borders: Technical challenges and policy solutions. International Medical Science Research Journal. 2024;4(7):788-96.
20. 23. Kim J, Kim J, Hur K, Choi E. EHRFL: Federated Learning Framework for Heterogeneous EHRs and Precision-guided Selection of Participating Clients. 2024;arXiv preprint arXiv:2404.13318.
21. 24. Yurdem B, Kuzlu M, Gullu MK, Catak FO, Tabassum M. Federated learning: Overview, strategies, applications, tools and future directions. Heliyon. 2024;20;10(19):e38137.
22. 25. Jimenez G, Solans D, Heikkila M, Vitaletti A, Kourtellis N, Anagnostopoulos A, Chatzigiannakis I. Non-IID data in Federated Learning: A Systematic Review with Taxonomy, Metrics, Methods, Frameworks and Future Directions. 2024;arXiv e-prints, arXiv:2411.12377.
23. 26. Yao Y, Zhang J, Wu J, Huang C, Xia Y, Yu T, Li A. Federated large language models: Current progress and future directions. 2024;arXiv preprint arXiv:2409.15723.