Processing, Please wait...

  • Publisher Home
  • Home
  • 🔙 Back
  • 📚 Journals
    • ⚙️ IJIEC - Industrial Engineering Computations
    • 🌐 IJDNS - Data and Network Science
    • 🧪 CCL - Current Chemistry Letters
    • 💹 AC - Accounting
    • 🎯 DSL - Decision Science Letters
    • 🚛 USCM - Uncertain Supply Chain Management
    • 🏗️ JPM - Journal of Project Management
    • 🏥 HE - Healthcare Engineering
    • 📈 SCI - Scientometrica
    • 🔩 ESM - Engineering Solid Mechanics
    • 🌿 JFS - Journal of Future Sustainability
    • 💼 MSL - Management Science Letters
  • 📝 Submit Article
  • 📊 Statistics
  • 📋 About
    • 📄 About Us
    • 📰 Blog
    • 📢 News
    • 📧 Contact
  • 📺 Tutorial
  • Search:
  • Advanced Search

Growing Science » Healthcare Engineering » Federated learning for healthcare: A bibliometric analysis of privacy-preserving machine learning applications in medical imaging, electronic health records, and clinical diagnostics based on 200 highly cited publications (2018–2025)

⭐ Highly Cited Articles

  • Jaya Algorithm
  • Rao Algorithm
  • TLBO Algorithm
  • ChatGPT and Blended Learning

Journals

  • IJIEC (840)
  • IJDS (1032)
  • DSL (759)
  • ESM (434)
  • CCL (563)
  • JPM (350)
  • AC (572)
  • JFS (106)
  • MSL (2658)
  • USCM (1104)
  • HE (54)
  • SCI (53)

HE Volumes

    • ▼ Volume 3 (12)
      • Issue 1 (5)
      • Issue 2 (5)
      • Issue 3 (2)
    • ▼ Volume 2 (21)
      • Issue 1 (5)
      • Issue 2 (6)
      • Issue 3 (5)
      • Issue 4 (5)
    • ▼ Volume 1 (21)
      • Issue 1 (6)
      • Issue 2 (5)
      • Issue 3 (5)
      • Issue 4 (5)

🔑 Keywords

Jordan(175)
Supply chain management(172)
Vietnam(154)
Customer satisfaction(124)
Performance(117)
Supply chain(115)
Artificial intelligence(108)
Service quality(101)
Competitive advantage(100)
SMEs(95)
Sustainability(94)
Tehran Stock Exchange(94)
optimization(88)
Financial performance(86)
TOPSIS(85)
Trust(85)
Genetic Algorithm(81)
Organizational performance(81)
Job satisfaction(81)
Social media(80)


» Show all keywords

✍️ Authors

Naser Azad(82)
Zeplin Jiwa Husada Tarigan(70)
Mohammad Reza Iravani(65)
Endri Endri(45)
Hotlan Siagian(43)
Muhammad Alshurideh(42)
Dmaithan Almajali(39)
Jumadil Saputra(37)
Muhammad Turki Alshurideh(35)
Ahmad Makui(33)
Sautma Ronni Basana(33)
Barween Al Kurdi(32)
Basrowi Basrowi(31)
Mohammad Khodaei Valahzaghard(30)
Haitham M. Alzoubi(30)
Ni Nyoman Kerti Yasa(30)
Hassan Ghodrati(30)
Shankar Chakraborty(29)
Sulieman Ibraheem Shelash Al-Hawary(28)
Mahmoud Allahham(28)


» Show all authors

🌍 Countries

1. Algeria (52)
2. Angola (2)
3. Argentina (22)
4. Armenia (2)
5. Australia (52)
6. Austria (2)
7. Bahrain (26)
8. Bangladesh (58)
9. Belarus (4)
10. Belgium (3)
11. Benin (2)
12. Benin Republic (1)
13. Bhutan (1)
14. Bosnia and Herzegovina (1)
15. Botswana (9)
16. Brazil (40)
17. Brunei (1)
18. Bulgaria (1)
19. Burkina Faso (1)
20. Cameroon (1)
Total: 121 countries

Show all countries
Healthcare Engineering
ISSN 3115-8269 (Online) - ISSN 3115-8250 (Print)
Quarterly Publication
Volume 2 Issue 4 pp. 217-242, 2026

Federated learning for healthcare: A bibliometric analysis of privacy-preserving machine learning applications in medical imaging, electronic health records, and clinical diagnostics based on 200 highly cited publications (2018–2025) Pages 217-242 PDF Download PDF

Authors: Reza Ghaeli

📋 Author Affiliations:
Reza Ghaeli ORCID 1
1 Computing Studies and Information Systems (CSIS), Faculty of Commerce & Business Administration (CBA), Douglas College, New West-minster, BC, Canada
doi 10.5267/j.ijdns.2026.38
Crossref 1 Source: CrossRef

🔑 Keywords: Federated Learning, Healthcare, Medical Imaging, Privacy Preservation, Electronic Health Records, Distributed Machine Learning, Differential Privacy, Blockchain, Digital Twin, Personalized Medicine

Abstract: This bibliometric survey examines 200 highly cited publications spanning from 2018 to 2025, retrieved from the Scopus database using search terms targeting federated learning applications in healthcare, medical imaging, and clinical diagnostics. The analysis reveals a rapidly maturing interdisciplinary field at the convergence of distributed machine learning, privacy-preserving technologies, and biomedical informatics. Key findings indicate that federated learning has emerged as a transformative paradigm for collaborative AI model training across healthcare institutions without compromising patient data privacy. The United States, China, the United Kingdom, Germany, and India emerge as the most productive nations, with extensive international collaboration networks reflecting the global need for privacy-compliant healthcare AI. Thematic clustering identifies five major research domains: (1) federated learning architectures and algorithms for healthcare, (2) privacy-preserving techniques including differential privacy, homomorphic encryption, and blockchain integration, (3) medical imaging applications (radiology, pathology, dermatology, ophthalmology), (4) electronic health records and predictive analytics, and (5) wearable devices and remote patient monitoring. Emerging trends include the integration of federated learning with explainable AI for clinical interpretability, the convergence of federated learning with digital twins and the healthcare metaverse, and the development of personalized federated learning frameworks for handling non-IID medical data. This survey provides a comprehensive mapping of the intellectual landscape, identifies persistent challenges including data heterogeneity, communication efficiency, and security vulnerabilities, and proposes future directions for privacy-preserving healthcare AI.

How to cite this paper
APA: Ghaeli, R. (2026). Federated learning for healthcare: A bibliometric analysis of privacy-preserving machine learning applications in medical imaging, electronic health records, and clinical diagnostics based on 200 highly cited publications (2018–2025). Healthcare Engineering, 2(4), 217-242.
Chicago/Turabian: Ghaeli, R. 2026. "Federated learning for healthcare: A bibliometric analysis of privacy-preserving machine learning applications in medical imaging, electronic health records, and clinical diagnostics based on 200 highly cited publications (2018–2025)." Healthcare Engineering 2, no. 4 (2026): 217-242.
AMA: Ghaeli, R. Federated learning for healthcare: A bibliometric analysis of privacy-preserving machine learning applications in medical imaging, electronic health records, and clinical diagnostics based on 200 highly cited publications (2018–2025). Healthcare Engineering. 2026;2(4):217-242.

References
Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024). Federated learning in smart healthcare: A comprehensive re-view on privacy, security, and predictive analytics with IoT integration. Healthcare, 12(24), 2587.
Abaoud, M., Almuqrin, M. A., & Khan, M. F. (2023). Advancing federated learning through novel mechanism for priva-cy preservation in healthcare applications. IEEE Access, 11, 83562–83579.
Adnan, M., Kalra, S., Cresswell, J. C., Taylor, G. W., & Tizhoosh, H. R. (2022). Federated learning and differential pri-vacy for medical image analysis. Scientific Reports, 12(1), 1953.
Ahamed, M. F., Hossain, M. M., Nahiduzzaman, M., Islam, M. R., Islam, M. R., Ahsan, M., & Haider, J. (2023). A re-view on brain tumor segmentation based on deep learning methods with federated learning techniques. Computerized Medical Imaging and Graphics, 110, 102313.
Akter, M., Moustafa, N., Lynar, T., & Razzak, I. (2022). Edge intelligence: Federated learning-based privacy protection framework for smart healthcare systems. IEEE Journal of Biomedical and Health Informatics, 26(12), 5805–5816.
Al-Huthaifi, R., Li, T., Huang, W., Gu, J., & Li, C. (2023). Federated learning in smart cities: Privacy and security sur-vey. Information Sciences, 632, 833–857.
Alazab, M., Rm, S. P., Parimala, M., Maddikunta, P. K. R., Gadekallu, T. R., & Pham, Q.-V. (2022). Federated learning for cybersecurity: Concepts, challenges, and future directions. IEEE Transactions on Industrial Informatics, 18(5), 3501–3509.
Albalawi, E., T. R., M., Thakur, A., Kumar, V. V., Gupta, M., Khan, S. B., & Almusharraf, A. (2024). Integrated ap-proach of federated learning with transfer learning for classification and diagnosis of brain tumor. BMC Medical Im-aging, 24(1), 110.
Albshaier, L., Almarri, S., & Albuali, A. (2025). Federated learning for cloud and edge security: A systematic review of challenges and AI opportunities. Electronics, 14(5), 1019.
Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023). Federated learning for privacy preservation in smart healthcare systems: A comprehensive survey. IEEE Journal of Biomedical and Health Informatics, 27(2), 778–789.
Alsamhi, S. H., Myrzashova, R., Hawbani, A., Kumar, S., Srivastava, S., Zhao, L., Wei, X., Guizan, M., & Curry, E. (2024). Federated learning meets blockchain in decentralized data sharing: Healthcare use case. IEEE Internet of Things Journal, 11(11), 19602–19615.
Alzubi, J. A., Alzubi, O. A., Singh, A., & Ramachandran, M. (2023). Cloud-IIoT-based electronic health record privacy-preserving by CNN and blockchain-enabled federated learning. IEEE Transactions on Industrial Informatics, 19(1), 1080–1087.
Andreux, M., du Terrail, J. O., Beguier, C., & Tramel, E. W. (2020). Siloed federated learning for multi-centric histo-pathology datasets. Lecture Notes in Computer Science, 12444 LNCS, 129–139.
Antunes, R. S., Da Costa, C. A., Küderle, A., Yari, I. A., & Eskofier, B. (2022). Federated learning for healthcare: Sys-tematic review and architecture proposal. ACM Transactions on Intelligent Systems and Technology, 13(4), 3501813.
Aouedi, O., Sacco, A., Piamrat, K., & Marchetto, G. (2023). Handling privacy-sensitive medical data with federated learning: Challenges and future directions. IEEE Journal of Biomedical and Health Informatics, 27(2), 790–803.
Arachchige, P. C. M., Bertok, P., Khalil, I., Liu, D., Camtepe, S., & Atiquzzaman, M. (2020). A trustworthy privacy pre-serving framework for machine learning in industrial IoT systems. IEEE Transactions on Industrial Informatics, 16(9), 6092–6102.
Arisdakessian, S., Wahab, O. A., Mourad, A., Otrok, H., & Guizani, M. (2023). A survey on IoT intrusion detection: Federated learning, game theory, social psychology, and explainable AI as future directions. IEEE Internet of Things Journal, 10(5), 4059–4092.
Arikumar, K. S., Prathiba, S. B., Alazab, M., Gadekallu, T. R., Pandya, S., Khan, J. M., & Moorthy, R. S. (2022). FL-PMI: Federated learning-based person movement identification through wearable devices in smart healthcare sys-tems. Sensors, 22(4), 1377.
Ashraf, E., Areed, N. F. F., Salem, H., Abdelhay, E. H., & Farouk, A. (2022). FIDChain: Federated intrusion detection system for blockchain-enabled IoT healthcare applications. Healthcare, 10(6), 1110.
Baghersalimi, S., Teijeiro, T., Atienza, D., & Aminifar, A. (2022). Personalized real-time federated learning for epilep-tic seizure detection. IEEE Journal of Biomedical and Health Informatics, 26(2), 898–909.
Bai, L., Hu, H., Ye, Q., Li, H., Wang, L., & Xu, J. (2024). Membership inference attacks and defenses in federated learn-ing: A survey. ACM Computing Surveys, 57(4), 3704633.
Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022). Federated learning review: Fundamentals, enabling technologies, and future applications. Information Processing and Management, 59(6), 103061.
Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M. (2023). Federated learning for the healthcare metaverse: Concepts, applica-tions, challenges, and future directions. IEEE Internet of Things Journal, 10(24), 21873–21891.
Baucas, M. J., Spachos, P., & Plataniotis, K. N. (2023). Federated learning and blockchain-enabled fog-IoT platform for wearables in predictive healthcare. IEEE Transactions on Computational Social Systems, 10(4), 1732–1741.
Bharati, S., Mondal, M. R. H., Podder, P., & Prasath, V. B. S. (2022). Federated learning: Applications, challenges and future directions. International Journal of Hybrid Intelligent Systems, 18(1-2), 19–35.
Bhati, D., Neha, F., & Amiruzzaman, M. (2024). A survey on explainable artificial intelligence (XAI) techniques for visualizing deep learning models in medical imaging. Journal of Imaging, 10(10), 239.
Bhatt, P., Liu, J., Gong, Y., Wang, J., & Guo, Y. (2022). Emerging artificial intelligence-empowered mHealth: Scoping review. JMIR mHealth and uHealth, 10(6), e35053.
Boobalan, P., Ramu, S. P., Pham, Q.-V., Dev, K., Pandya, S., Maddikunta, P. K. R., Gadekallu, T. R., & Huynh-The, T. (2022). Fusion of federated learning and industrial Internet of Things: A survey. Computer Networks, 212, 109048.
Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018). Federated learning of predictive models from federated electronic health records. International Journal of Medical Informatics, 112, 59–67.
Camajori Tedeschini, B., Savazzi, S., Stoklasa, R., Barbieri, L., Stathopoulos, I., Nicoli, M., & Serio, L. (2022). Decen-tralized federated learning for healthcare networks: A case study on tumor segmentation. IEEE Access, 10, 8693–8708.
Campos, E. M., Saura, P. F., González-Vidal, A., Hernández-Ramos, J. L., Bernabé, J. B., Baldini, G., & Skarmeta, A. (2022). Evaluating federated learning for intrusion detection in Internet of Things: Review and challenges. Computer Networks, 203, 108661.
Can, Y. S., & Ersoy, C. (2021). Privacy-preserving federated deep learning for wearable IoT-based biomedical monitor-ing. ACM Transactions on Internet Technology, 21(1), 21.
Castiglioni, I., Rundo, L., Codari, M., Di Leo, G., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D'Amico, N. C., & Sardanelli, F. (2021). AI applications to medical images: From machine learning to deep learning. Physica Medica, 83, 9–24.
Chamikara, M. A. P., Bertok, P., Khalil, I., Liu, D., & Camtepe, S. (2021). Privacy preserving distributed machine learn-ing with federated learning. Computer Communications, 171, 112–125.
Chang, Y., Fang, C., & Sun, W. (2021). A blockchain-based federated learning method for smart healthcare. Computational Intelligence and Neuroscience, 2021, 4376418.
Chen, J., Yi, C., Okegbile, S. D., Cai, J., & Shen, X. (2024). Networking architecture and key supporting technologies for human digital twin in personalized healthcare: A comprehensive survey. IEEE Communications Surveys and Tu-torials, 26(1), 706–746.
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023). Al-gorithmic fairness in artificial intelligence for medicine and healthcare. Nature Biomedical Engineering, 7(6), 719–742.
Chen, Y., Lu, W., Qin, X., Wang, J., & Xie, X. (2024). MetaFed: Federated learning among federations with cyclic knowledge distillation for personalized healthcare. IEEE Transactions on Neural Networks and Learning Systems, 35(11), 16671–16682.
Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020). FedHealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems, 35(4), 83–93.
Chen, Z., Yang, C., Zhu, M., Peng, Z., & Yuan, Y. (2022). Personalized retrogress-resilient federated learning toward imbalanced medical data. IEEE Transactions on Medical Imaging, 41(12), 3663–3674.
Conze, P.-H., Andrade-Miranda, G., Singh, V. K., Jaouen, V., & Visvikis, D. (2023). Current and emerging trends in medical image segmentation with deep learning. IEEE Transactions on Radiation and Plasma Medical Sciences, 7(6), 545–569.
Crowson, M. G., Moukheiber, D., Arévalo, A. R., Lam, B. D., Mantena, S., Rana, A., Goss, D., Bates, D. W., & Celi, L. A. (2022). A systematic review of federated learning applications for biomedical data. PLOS Digital Health, 1(5 May), e0000033.
Dang, T. K., Lan, X., Weng, J., & Feng, M. (2022). Federated learning for electronic health records. ACM Transactions on Intelligent Systems and Technology, 13(5), 72.
Darzidehkalani, E., Ghasemi-rad, M., & van Ooijen, P. M. A. (2022). Federated learning in medical imaging: Part II: Methods, challenges, and considerations. Journal of the American College of Radiology, 19(8), 975–982.
Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., Wang, C.-H., Hsu, C.-N., Lee, C. K., Ruan, P., Xu, D., Wu, D., Huang, E., Kitamura, F. C., Lacey, G., … Li, Q. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27(10), 1735–1743.
Deist, T. M., Dankers, F. J. W. M., Ojha, P., Scott Marshall, M., Janssen, T., Faivre-Finn, C., Masciocchi, C., Valentini, V., Wang, J., Chen, J., Zhang, Z., Spezi, E., Button, M., Jan Nuyttens, J., Vernhout, R., van Soest, J., Jochems, A., Monshouwer, R., Bussink, J., … Dekker, A. (2020). Distributed learning on 20,000+ lung cancer patients – The per-sonal health train. Radiotherapy and Oncology, 144, 189–200.
Dhasaratha, C., Hasan, M. K., Islam, S., Khapre, S., Abdullah, S., Ghazal, T. M., Alzahrani, A. I., Alalwan, N., Vo, N., & Akhtaruzzaman, M. (2024). Data privacy model using blockchain reinforcement federated learning approach for scalable internet of medical things. CAAI Transactions on Intelligence Technology. Advance online publication.
Dou, Q., So, T. Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Kaissis, G., Li, Z., Si, W., Lee, H. H. C., Yu, K., Feng, Z., Dong, L., Burian, E., Jungmann, F., Braren, R., Makowski, M., Kainz, B., Rueckert, D., Glocker, B., … Heng, P. A. (2021). Federated deep learning for detecting COVID-19 lung abnormalities in CT: A privacy-preserving multina-tional validation study. npj Digital Medicine, 4(1), 60.
du Terrail, J. O., Ayed, S.-S., Cyffers, E., Grimberg, F., He, C., Loeb, R., Mangold, P., Marchand, T., Marfoq, O., Mush-taq, E., Muzellec, B., Philippenko, C., Silva, S., Teleńczuk, M., Albarqouni, S., Avestimehr, S., Bellet, A., Dieule-veut, A., Jaggi, M., … Andreux, M. (2022). FLamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings. Advances in Neural Information Processing Systems, 35.
Elayan, H., Aloqaily, M., & Guizani, M. (2022). Sustainability of healthcare data analysis IoT-based systems using deep federated learning. IEEE Internet of Things Journal, 9(10), 7338–7346.
Ezzeldin, Y. H., Yan, S., He, C., Ferrara, E., & Avestimehr, S. (2023). FairFed: Enabling group fairness in federated learning. Proceedings of the 37th AAAI Conference on Artificial Intelligence, 37(6), 7494–7502.
Feki, I., Ammar, S., Kessentini, Y., & Muhammad, K. (2021). Federated learning for COVID-19 screening from chest X-ray images. Applied Soft Computing, 106, 107330.
Feng, Y., Ma, B., Zhang, J., Zhao, S., Xia, Y., & Tao, D. (2022). FIBA: Frequency-injection based backdoor attack in medical image analysis. *Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022-June*, 20844–20853.
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021). Federated deep learning for cyber security in the Internet of Things: Concepts, applications, and experimental analysis. IEEE Access, 9, 138509–138542.
Ficco, M., Guerriero, A., Milite, E., Palmieri, F., Pietrantuono, R., & Russo, S. (2024). Federated learning for IoT devic-es: Enhancing TinyML with on-board training. Information Fusion, 104, 102189.
Ficili, I., Giacobbe, M., Tricomi, G., & Puliafito, A. (2025). From sensors to data intelligence: Leveraging IoT, cloud, and edge computing with AI. Sensors, 25(6), 1763.
Firouzi, F., Jiang, S., Chakrabarty, K., Farahani, B., Daneshmand, M., Song, J., & Mankodiya, K. (2023). Fusion of IoT, AI, edge-fog-cloud, and blockchain: Challenges, solutions, and a case study in healthcare and medicine. IEEE Inter-net of Things Journal, 10(5), 3686–3705.
Foley, P., Sheller, M. J., Edwards, B., Pati, S., Riviera, W., Sharma, M., Narayana Moorthy, P., Wang, S.-H., Martin, J., Mirhaji, P., Shah, P., & Bakas, S. (2022). OpenFL: The open federated learning library. Physics in Medicine and Bi-ology, 67(21), 214001.
Gebremariam, G. G., Panda, J., & Indu, S. (2023). Blockchain-based secure localization against malicious nodes in IoT-based wireless sensor networks using federated learning. Wireless Communications and Mobile Computing, 2023, 8068038.
Gong, X., Sharma, A., Karanam, S., Wu, Z., Chen, T., Doermann, D., & Innanje, A. (2021). Ensemble attention distilla-tion for privacy-preserving federated learning. Proceedings of the IEEE International Conference on Computer Vi-sion, 15056–15066.
Gu, X., Sabrina, F., Fan, Z., & Sohail, S. (2023). A review of privacy enhancement methods for federated learning in healthcare systems. International Journal of Environmental Research and Public Health, 20(15), 6539.
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024). Federated learning for medical image analysis: A survey. Pattern Recognition, 151, 110424.
Guduri, M., Chakraborty, C., Maheswari, U., & Margala, M. (2024). Blockchain-based federated learning technique for privacy preservation and security of smart electronic health records. IEEE Transactions on Consumer Electronics, 70(1), 2608–2617.
Guo, P., Wang, P., Zhou, J., Jiang, S., & Patel, V. M. (2021). Multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning. Proceedings of the IEEE Comput-er Society Conference on Computer Vision and Pattern Recognition, 2423–2432.
Gupta, D., Kayode, O., Bhatt, S., Gupta, M., & Tosun, A. S. (2021). Hierarchical federated learning based anomaly de-tection using digital twins for smart healthcare. Proceedings of the 2021 IEEE 7th International Conference on Col-laboration and Internet Computing, CIC 2021, 16–25.
Hao, M., Li, H., Luo, X., Xu, G., Yang, H., & Liu, S. (2020). Efficient and privacy-enhanced federated learning for in-dustrial artificial intelligence. IEEE Transactions on Industrial Informatics, 16(10), 6532–6542.
Hatamizadeh, A., Yin, H., Molchanov, P., Myronenko, A., Li, W., Dogra, P., Feng, A., Flores, M. G., Kautz, J., Xu, D., & Roth, H. R. (2023). Do gradient inversion attacks make federated learning unsafe? IEEE Transactions on Medical Imaging, 42(7), 2044–2056.
Hauschild, A.-C., Lemanczyk, M., Matschinske, J., Frisch, T., Zolotareva, O., Holzinger, A., Baumbach, J., & Heider, D. (2022). Federated random forests can improve local performance of predictive models for various healthcare appli-cations. Bioinformatics, 38(8), 2278–2286.
Heidari, A., Javaheri, D., Toumaj, S., Navimipour, N. J., Rezaei, M., & Unal, M. (2023). A new lung cancer detection method based on the chest CT images using federated learning and blockchain systems. Artificial Intelligence in Medicine, 141, 102572.
Hijazi, N. M., Aloqaily, M., Guizani, M., Ouni, B., & Karray, F. (2024). Secure federated learning with fully homomor-phic encryption for IoT communications. IEEE Internet of Things Journal, 11(3), 4289–4300.
Hossen, M. N., Panneerselvam, V., Koundal, D., Ahmed, K., Bui, F. M., & Ibrahim, S. M. (2023). Federated machine learning for detection of skin diseases and enhancement of internet of medical things (IoMT) security. IEEE Journal of Biomedical and Health Informatics, 27(2), 835–841.
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., & Zitnik, M. (2021). Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development. Advances in Neural Information Processing Systems.
Huang, Z.-A., Hu, Y., Liu, R., Xue, X., Zhu, Z., Song, L., & Tan, K. C. (2023). Federated multi-task learning for joint di-agnosis of multiple mental disorders on MRI scans. IEEE Transactions on Biomedical Engineering, 70(4), 1137–1149.
Jabarulla, M. Y., & Lee, H.-N. (2021). A blockchain and artificial intelligence-based, patient-centric healthcare system for combating the COVID-19 pandemic: Opportunities and applications. Healthcare, 9(8), 1019.
Javeed, D., Saeed, M. S., Kumar, P., Jolfaei, A., Islam, S., & Islam, A. K. M. N. (2024). Federated learning-based per-sonalized recommendation systems: An overview on security and privacy challenges. IEEE Transactions on Con-sumer Electronics, 70(1), 2618–2627.
Jawadur Rahman, K. M., Ahmed, F., Akhter, N., Hasan, M., Amin, R., Aziz, K. E., Muzahidul Islam, A. K. M., Mukta, M. S. H., & Najmul Islam, A. K. M. (2021). Challenges, applications and design aspects of federated learning: A survey. IEEE Access, 9, 124682–124700.
Jiang, M., Wang, Z., & Dou, Q. (2022). HarmoFL: Harmonizing local and global drifts in federated learning on hetero-geneous medical images. Proceedings of the 36th AAAI Conference on Artificial Intelligence, 36(1), 914–922.
Jiménez-Sánchez, A., Tardy, M., González Ballester, M. A., Mateus, D., & Piella, G. (2023). Memory-aware curriculum federated learning for breast cancer classification. Computer Methods and Programs in Biomedicine, 229, 107318.
Joshi, M., Pal, A., & Sankarasubbu, M. (2022). Federated learning for healthcare domain - Pipeline, applications and challenges. ACM Transactions on Computing for Healthcare, 3(4), 3533708.
Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Jr., Mancuso, J., Jungmann, F., Steinborn, M.-M., Saleh, A., Makowski, M., Rueckert, D., & Braren, R. (2021). End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence, 3(6), 473–484.
Kalra, S., Wen, J., Cresswell, J. C., Volkovs, M., & Tizhoosh, H. R. (2023). Decentralized federated learning through proxy model sharing. Nature Communications, 14(1), 2899.
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 158, 106848.
Khan, I. A., Razzak, I., Pi, D., Khan, N., Hussain, Y., Li, B., & Kousar, T. (2024). Fed-Inforce-Fusion: A federated rein-forcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks. Information Fusion, 101, 102002.
Khan, L. U., Han, Z., Saad, W., Hossain, E., Guizani, M., & Hong, C. S. (2022). Digital twin of wireless systems: Over-view, taxonomy, challenges, and opportunities. IEEE Communications Surveys and Tutorials, 24(4), 2230–2264.
Khoa, T. V., Saputra, Y. M., Hoang, D. T., Trung, N. L., Nguyen, D., Ha, N. V., & Dutkiewicz, E. (2020). Collaborative learning model for cyberattack detection systems in IoT industry 4.0. *IEEE Wireless Communications and Net-working Conference, WCNC, 2020-May*, 9120761.
Krones, F., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2025). Review of multimodal machine learning ap-proaches in healthcare. Information Fusion, 114, 102690.
Kumar, Y., & Singla, R. (2021). Federated learning systems for healthcare: Perspective and recent progress. Studies in Computational Intelligence, 965, 141–156.
Lakhan, A., Mohammed, M. A., Abdulkareem, K. H., Hamouda, H., & Alyahya, S. (2023). Autism spectrum disorder de-tection framework for children based on federated learning integrated CNN-LSTM. Computers in Biology and Medi-cine, 166, 107539.
Lakhan, A., Mohammed, M. A., Nedoma, J., Martinek, R., Tiwari, P., Vidyarthi, A., Alkhayyat, A., & Wang, W. (2023). Federated-learning based privacy preservation and fraud-enabled blockchain IoMT system for healthcare. IEEE Journal of Biomedical and Health Informatics, 27(2), 664–672.
Lee, G. H., & Shin, S.-Y. (2020). Federated learning on clinical benchmark data: Performance assessment. Journal of Medical Internet Research, 22(10), e20891.
Li, D., Luo, Z., & Cao, B. (2022). Blockchain-based federated learning methodologies in smart environments. Cluster Computing, 25(4), 2585–2599.
Li, H., Li, C., Wang, J., Yang, A., Ma, Z., Zhang, Z., & Hua, D. (2023). Review on security of federated learning and its application in healthcare. Future Generation Computer Systems, 144, 271–290.
Li, J., Meng, Y., Ma, L., Du, S., Zhu, H., Pei, Q., & Shen, X. (2022). A federated learning based privacy-preserving smart healthcare system. IEEE Transactions on Industrial Informatics, 18(3), 2021–2031.
Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., & Feng, A. (2019). Privacy-preserving federated brain tumour segmentation. Lecture Notes in Computer Science, 11861 LNCS, 133–141.
Li, X., Jiang, M., Zhang, X., Kamp, M., & Dou, Q. (2021). FedBN: Federated learning on non-IID features via local batch normalization. *ICLR 2021 - 9th International Conference on Learning Representations*.
Lian, Z., Yang, Q., Wang, W., Zeng, Q., Alazab, M., Zhao, H., & Su, C. (2022). DEEP-FEL: Decentralized, efficient and privacy-enhanced federated edge learning for healthcare cyber physical systems. IEEE Transactions on Network Sci-ence and Engineering, 9(5), 3558–3569.
Liang, P. P., Lyu, Y., Fan, X., Wu, Z., Cheng, Y., Wu, J., Chen, L., Wu, P., Lee, M. A., Zhu, Y., Salakhutdinov, R., & Morency, L.-P. (2021). MULTIBENCH: Multiscale benchmarks for multimodal representation learning. Advances in Neural Information Processing Systems.
Liang, X., Lin, Y., Fu, H., Zhu, L., & Li, X. (2022). RSCFed: Random sampling consensus federated semi-supervised learning. *Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022-June*, 10144–10153.
Lim, W. Y. B., Garg, S., Xiong, Z., Niyato, D., Leung, C., Miao, C., & Guizani, M. (2021). Dynamic contract design for federated learning in smart healthcare applications. IEEE Internet of Things Journal, 8(23), 16853–16862.
Liu, Q., Chen, C., Qin, J., Dou, Q., & Heng, P.-A. (2021). FEDDG: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. Proceedings of the IEEE Computer Society Con-ference on Computer Vision and Pattern Recognition, 1013–1023.
Liu, Q., Yang, H., Dou, Q., & Heng, P.-A. (2021). Federated semi-supervised medical image classification via inter-client relation matching. Lecture Notes in Computer Science, 12903 LNCS, 325–335.
Liu, X., Gao, K., Liu, B., Pan, C., Liang, K., Yan, L., Ma, J., He, F., Zhang, S., Pan, S., & Yu, Y. (2021). Advances in deep learning-based medical image analysis. Health Data Science, 2021, 8786793.
Liu, X., Zhao, J., Li, J., Cao, B., & Lv, Z. (2022). Federated neural architecture search for medical data security. IEEE Transactions on Industrial Informatics, 18(8), 5628–5636.
Liu, Z., Chen, Y., Zhao, Y., Yu, H., Liu, Y., Bao, R., Jiang, J., Nie, Z., Xu, Q., & Yang, Q. (2022). Contribution-aware federated learning for smart healthcare. Proceedings of the 36th AAAI Conference on Artificial Intelligence, 36, 12396–12404.
Lo, J., Yu, T. T., Ma, D., Zang, P., Owen, J. P., Zhang, Q., Wang, R. K., Beg, M. F., Lee, A. Y., Jia, Y., & Sarunic, M. V. (2021). Federated learning for microvasculature segmentation and diabetic retinopathy classification of OCT da-ta. Ophthalmology Science, 1(4), 100069.
Loghin, D., Cai, S., Chen, G., Dinh, T. T. A., Fan, F., Lin, Q., Ng, J., Ooi, B. C., Sun, X., Ta, Q.-T., Wang, W., Xiao, X., Yang, Y., Zhang, M., & Zhang, Z. (2020). The disruptions of 5G on data-driven technologies and applications. IEEE Transactions on Knowledge and Data Engineering, 32(6), 1179–1198.
Luo, J., & Wu, S. (2022). Adapt to adaptation: Learning personalization for cross-silo federated learning. IJCAI Interna-tional Joint Conference on Artificial Intelligence, 2166–2173.
Ma, J., Naas, S.-A., Sigg, S., & Lyu, X. (2022). Privacy-preserving federated learning based on multi-key homomorphic encryption. International Journal of Intelligent Systems, 37(9), 5880–5901.
Malik, H., Naeem, A., Naqvi, R. A., & Loh, W.-K. (2023). DMFL_Net: A federated learning-based framework for the classification of COVID-19 from multiple chest diseases using X-rays. Sensors, 23(2), 743.
Menon U, V., Babu Kumaravelu, V., Kumar, V. C., Rammohan, A., Chinnadurai, S., Venkatesan, R., Hai, H., & Selvaprabhu, P. (2025). AI-powered IoT: A survey on integrating artificial intelligence with IoT for enhanced securi-ty, efficiency, and smart applications. IEEE Access, 13, 50296–50339.
Messinis, S., Temenos, N., Protonotarios, N. E., Rallis, I., Kalogeras, D., & Doulamis, N. (2024). Enhancing internet of medical things security with artificial intelligence: A comprehensive review. Computers in Biology and Medicine, 170, 108036.
Mikołajewska, E., Mikołajewski, D., Mikołajczyk, T., & Paczkowski, T. (2025). Generative AI in AI-based digital twins for fault diagnosis for predictive maintenance in industry 4.0/5.0. Applied Sciences, 15(6), 3166.
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023). Reviewing federated machine learning and its use in diseases prediction. Sensors, 23(4), 2112.
Moulahi, W., Jdey, I., Moulahi, T., Alawida, M., & Alabdulatif, A. (2023). A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data. Computers in Biology and Medicine, 167, 107630.
Muhammed, D., Ahvar, E., Ahvar, S., Trocan, M., Montpetit, M.-J., & Ehsani, R. (2024). Artificial intelligence of things (AIoT) for smart agriculture: A review of architectures, technologies and solutions. Journal of Network and Comput-er Applications, 228, 103905.
Myrzashova, R., Alsamhi, S. H., Shvetsov, A. V., Hawbani, A., & Wei, X. (2023). Blockchain meets federated learning in healthcare: A systematic review with challenges and opportunities. IEEE Internet of Things Journal, 10(16), 14418–14437.
Nakach, F.-Z., Idri, A., & Goceri, E. (2024). A comprehensive investigation of multimodal deep learning fusion strate-gies for breast cancer classification. Artificial Intelligence Review, 57(12), 327.
Nazir, S., & Kaleem, M. (2023). Federated learning for medical image analysis with deep neural networks. Diagnostics, 13(9), 1532.
Ng, D., Lan, X., Yao, M. M.-S., Chan, W. P., & Feng, M. (2021). Federated learning: A collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets. Quantitative Imaging in Medi-cine and Surgery, 11(2), 852–857.
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021). Federated learning for In-ternet of Things: A comprehensive survey. IEEE Communications Surveys and Tutorials, 23(3), 1622–1658.
Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2020). Blockchain for 5G and beyond networks: A state of the art survey. Journal of Network and Computer Applications, 166, 102693.
Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2022). Federated learning for smart healthcare: A survey. ACM Computing Surveys, 55(3), 3501296.
Ogier du Terrail, J., Leopold, A., Joly, C., Béguier, C., Andreux, M., Maussion, C., Schmauch, B., Tramel, E. W., Bendjebbar, E., Zaslavskiy, M., Wainrib, G., Milder, M., Gervasoni, J., Guerin, J., Durand, T., Livartowski, A., Mou-tet, K., Gautier, C., Djafar, I., … Heudel, P.-E. (2023). Federated learning for predicting histological response to ne-oadjuvant chemotherapy in triple-negative breast cancer. Nature Medicine, 29(1), 135–146.
Oyebode, O., Fowles, J., Steeves, D., & Orji, R. (2023). Machine learning techniques in adaptive and personalized sys-tems for health and wellness. *International Journal of Human-Computer Interaction, 39*(9), 1938–1962.
Pandya, S., Srivastava, G., Jhaveri, R., Babu, M. R., Bhattacharya, S., Maddikunta, P. K. R., Mastorakis, S., Piran, M. J., & Gadekallu, T. R. (2023). Federated learning for smart cities: A comprehensive survey. Sustainable Energy Tech-nologies and Assessments, 55, 102987.
Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., Sako, C., Ghodasara, S., Bilello, M., Mohan, S., Vollmuth, P., Brugnara, G., Preetha, C. J., Sahm, F., Maier-Hein, K., … Bakas, S. (2022). Federated learning enables big data for rare cancer boundary detection. Nature Communica-tions, 13(1), 7346.
Pati, S., Kumar, S., Varma, A., Edwards, B., Lu, C., Qu, L., Wang, J. J., Lakshminarayanan, A., Wang, S.-H., Sheller, M. J., Chang, K., Singh, P., Rubin, D. L., Kalpathy-Cramer, J., & Bakas, S. (2024). Privacy preservation for federated learning in health care. Patterns, 5(7), 100974.
Peng, L., Wang, N., Dvornek, N., Zhu, X., & Li, X. (2023). FedNI: Federated graph learning with network inpainting for population-based disease prediction. IEEE Transactions on Medical Imaging, 42(7), 2032–2043.
Peng, Z., Xu, J., Chu, X., Gao, S., Yao, Y., Gu, R., & Tang, Y. (2022). VFChain: Enabling verifiable and auditable feder-ated learning via blockchain systems. IEEE Transactions on Network Science and Engineering, 9(1), 173–186.
Pfitzner, B., Steckhan, N., & Arnrich, B. (2021). Federated learning in a medical context: A systematic literature re-view. ACM Transactions on Internet Technology, 21(2), 3412357.
Prayitno, Shyu, C.-R., Putra, K. T., Chen, H.-C., Tsai, Y.-Y., Tozammel Hossain, K. S. M., Jiang, W., & Shae, Z.-Y. (2021). A systematic review of federated learning in the healthcare area: From the perspective of data properties and applications. Applied Sciences, 11(23), 11191.
Putra, K. T., Arrayyan, A. Z., Hayati, N., Firdaus, Damarjati, C., Bakar, A., & Chen, H.-C. (2024). A review on the ap-plication of internet of medical things in wearable personal health monitoring: A cloud-edge artificial intelligence approach. IEEE Access, 12, 21437–21452.
Qayyum, A., Ahmad, K., Ahsan, M. A., Al-Fuqaha, A., & Qadir, J. (2022). Collaborative federated learning for healthcare: Multi-modal COVID-19 diagnosis at the edge. IEEE Open Journal of the Computer Society, 3, 172–184.
Qiu, L., Cheng, J., Gao, H., Xiong, W., & Ren, H. (2023). Federated semi-supervised learning for medical image seg-mentation via pseudo-label denoising. IEEE Journal of Biomedical and Health Informatics, 27(10), 4672–4683.
Qureshi, R., Irfan, M., Ali, H., Khan, A., Nittala, A. S., Ali, S., Shah, A., Gondal, T. M., Sadak, F., Shah, Z., Hadi, M. U., Khan, S., Al-Tashi, Q., Wu, J., Bermak, A., & Alam, T. (2023). Artificial intelligence and biosensors in healthcare and its clinical relevance: A review. IEEE Access, 11, 61600–61620.
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023). Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues. Cluster Computing, 26(4), 2271–2311.
Rahman, M. A., Shamim Hossain, M., Islam, M. S., Alrajeh, N. A., & Muhammad, G. (2020). Secure and provenance enhanced internet of health things framework: A blockchain managed federated learning approach. IEEE Access, 8, 205071–205087.
Rajagopal, S. M., Supriya, M., & Buyya, R. (2023). FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated edge-fog-cloud computing environments. Internet of Things, 22, 100784.
Ramu, S. P., Boopalan, P., Pham, Q.-V., Maddikunta, P. K. R., Huynh-The, T., Alazab, M., Nguyen, T. T., & Gadekallu, T. R. (2022). Federated learning enabled digital twins for smart cities: Concepts, recent advances, and future direc-tions. Sustainable Cities and Society, 79, 103663.
Rani, S., Kataria, A., Kumar, S., & Tiwari, P. (2023). Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review. *Knowledge-Based Systems, 274*, 110658.
Rauniyar, A., Hagos, D. H., Jha, D., Hakegard, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024). Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions. IEEE Internet of Things Journal, 11(5), 7374–7398.
Raza, A., Tran, K. P., Koehl, L., & Li, S. (2022). Designing ECG monitoring healthcare system with federated transfer learning and explainable AI. *Knowledge-Based Systems, 236*, 107763.
Rehman, A., Abbas, S., Khan, M. A., Ghazal, T. M., Adnan, K. M., & Mosavi, A. (2022). A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique. Computers in Biology and Medicine, 150, 106019.
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3(1), 119.
Roth, H. R., Chang, K., Singh, P., Neumark, N., Li, W., Gupta, V., Gupta, S., Qu, L., Ihsani, A., Bizzo, B. C., Wen, Y., Buch, V., Shah, M., Kitamura, F., Mendonça, M., Lavor, V., Harouni, A., Compas, C., Tetreault, J., … Kalpathy-Cramer, J. (2020). Federated learning for breast density classification: A real-world implementation. Lecture Notes in Computer Science, 12444 LNCS, 181–191.
Sadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., Ingerman, A., Mellem, S., Kairouz, P., Nsoesie, E. O., MacFarlane, J., Vullikanti, A., Marathe, M., Eastham, P., Brownstein, J. S., Arcas, B. A., Howell, M. D., & Hernandez, J. (2021). Privacy-first health research with federated learning. npj Digital Medicine, 4(1), 132.
Salam, M. A., Taha, S., & Ramadan, M. (2021). COVID-19 detection using federated machine learning. PLoS ONE, 16(6 June), e0252573.
Samuel, O., Omojo, A. B., Onuja, A. M., Sunday, Y., Tiwari, P., Gupta, D., Hafeez, G., Yahaya, A. S., Fatoba, O. J., & Shamshirband, S. (2023). IoMT: A COVID-19 healthcare system driven by federated learning and blockchain. IEEE Journal of Biomedical and Health Informatics, 27(2), 823–834.
Saraswat, D., Bhattacharya, P., Verma, A., Prasad, V. K., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R. (2022). Explainable AI for healthcare 5.0: Opportunities and challenges. IEEE Access, 10, 84486–84517.
Saraswat, D., Verma, A., Bhattacharya, P., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R. (2022). Blockchain-based federated learning in UAVs beyond 5G networks: A solution taxonomy and future directions. IEEE Access, 10, 33154–33182.
Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., Wood, B. J., Choyke, P. L., Priester, A. M., Marks, L. S., Raman, S. S., Enzmann, D., Turkbey, B., Speier, W., & Arnold, C. W. (2021). Federated learning improves site performance in multicenter deep learning without data shar-ing. Journal of the American Medical Informatics Association, 28(6), 1259–1264.
Schwendicke, F., Samek, W., & Krois, J. (2020). Artificial intelligence in dentistry: Chances and challenges. Journal of Dental Research, 99(7), 769–774.
Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., & Acharya, U. R. (2023). Remote patient monitoring us-ing artificial intelligence: Current state, applications, and challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2), e1485.
Sharma, S., & Guleria, K. (2023). A comprehensive review on federated learning based models for healthcare applica-tions. Artificial Intelligence in Medicine, 146, 102691.
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., & Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10(1), 12598.
Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019). Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. Lecture Notes in Computer Science, 11383 LNCS, 92–104.
Shen, Y., Zhou, Y., & Yu, L. (2022). CD2-pFed: Cyclic distillation-guided channel decoupling for model personaliza-tion in federated learning. *Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022-June*, 10031–10040.
Silva, S., Gutman, B. A., Romero, E., Thompson, P. M., Altmann, A., & Lorenzi, M. (2019). Federated learning in dis-tributed medical databases: Meta-analysis of large-scale subcortical brain data. *Proceedings - International Sympo-sium on Biomedical Imaging, 2019-April*, 270–274.
Singh, P., Gaba, G. S., Kaur, A., Hedabou, M., & Gurtov, A. (2023). Dew-cloud-based hierarchical federated learning for intrusion detection in IoMT. IEEE Journal of Biomedical and Health Informatics, 27(2), 722–731.
Singh, S., Rathore, S., Alfarraj, O., Tolba, A., & Yoon, B. (2022). A framework for privacy-preservation of IoT healthcare data using federated learning and blockchain technology. Future Generation Computer Systems, 129, 380–388.
Sirohi, D., Kumar, N., Rana, P. S., Tanwar, S., Iqbal, R., & Hijjii, M. (2023). Federated learning for 6G-enabled secure communication systems: A comprehensive survey. Artificial Intelligence Review, 56(10), 11297–11389.
Sohan, M. F., & Basalamah, A. (2023). A systematic review on federated learning in medical image analysis. IEEE Ac-cess, 11, 28628–28644.
Sun, B., Huo, H., Yang, Y., & Bai, B. (2021). PartialFed: Cross-domain personalized federated learning via partial ini-tialization. Advances in Neural Information Processing Systems, 28, 23309–23320.
Tabassum, A., Erbad, A., Lebda, W., Mohamed, A., & Guizani, M. (2022). FEDGAN-IDS: Privacy-preserving IDS using GAN and federated learning. Computer Communications, 192, 299–310.
Tan, Y. N., Tinh, V. P., Lam, P. D., Nam, N. H., & Khoa, T. A. (2023). A transfer learning approach to breast cancer classification in a federated learning framework. IEEE Access, 11, 27462–27476.
Teo, Z. L., Jin, L., Li, S., Miao, D., Zhang, X., Ng, W. Y., Tan, T. F., Lee, D. M., Chua, K. J., Heng, J., Liu, Y., Goh, R. S. M., & Ting, D. S. W. (2024). Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture. Cell Reports Medicine, 5(2), 101419.
Tian, Y., Wang, S., Xiong, J., Bi, R., Zhou, Z., & Bhuiyan, M. Z. A. (2024). Robust and privacy-preserving decentralized deep federated learning training: Focusing on digital healthcare applications. *IEEE/ACM Transactions on Compu-tational Biology and Bioinformatics, 21*(4), 890–901.
Tiwari, A., Mishra, S., & Kuo, T.-R. (2025). Current AI technologies in cancer diagnostics and treatment. Molecular Cancer, 24(1), 159.
Tiwari, P., Lakhan, A., Jhaveri, R. H., & Gronli, T.-M. (2023). Consumer-centric internet of medical things for cyborg applications based on federated reinforcement learning. IEEE Transactions on Consumer Electronics, 69(4), 756–764.
Triastcyn, A., & Faltings, B. (2019). Federated learning with Bayesian differential privacy. Proceedings of the 2019 IEEE International Conference on Big Data, Big Data 2019, 2587–2596.
Ullah, F., Nadeem, M., Abrar, M., Amin, F., Salam, A., & Khan, S. (2023). Enhancing brain tumor segmentation accura-cy through scalable federated learning with advanced data privacy and security measures. Mathematics, 11(19), 4189.
Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., de Freitas, J. K., Wanyan, T., Johnson, K. W., Bicak, M., Klang, E., Kwon, Y. J., Costa, A., Zhao, S., Miotto, R., Charney, A. W., Böttinger, E., … Glicksberg, B. S. (2021). Federated learning of electronic health records to improve mortality prediction in hospital-ized patients with COVID-19: Machine learning approach. JMIR Medical Informatics, 9(1), e24207.
Verma, S., Boonsanong, V., Hoang, M., Hines, K., Dickerson, J., & Shah, C. (2024). Counterfactual explanations and al-gorithmic recourses for machine learning: A review. ACM Computing Surveys, 56(12), 312.
Wang, B., Li, H., Guo, Y., & Wang, J. (2023). PPFLHE: A privacy-preserving federated learning scheme with homo-morphic encryption for healthcare data. Applied Soft Computing, 146, 110677.
Wang, F., & Preininger, A. (2019). AI in health: State of the art, challenges, and future directions. Yearbook of Medical Informatics, 28(1), 16–26.
Wang, R., Lai, J., Zhang, Z., Li, X., Vijayakumar, P., & Karuppiah, M. (2023). Privacy-preserving federated learning for internet of medical things under edge computing. IEEE Journal of Biomedical and Health Informatics, 27(2), 854–865.
Wang, W., Li, X., Qiu, X., Zhang, X., Zhao, J., & Brusic, V. (2023). A privacy preserving framework for federated learning in smart healthcare systems. Information Processing and Management, 60(1), 103167.
Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N. A., Ktena, S., Tran, F., Bitzer, M., Ossowski, S., Casadei, N., Herr, C., Petersheim, D., Beh-rends, U., Kern, F., … Schultze, J. L. (2021). Swarm learning for decentralized and confidential clinical machine learning. Nature, 594(7862), 265–270.
Wicaksana, J., Yan, Z., Zhang, D., Huang, X., Wu, H., Yang, X., & Cheng, K.-T. (2023). FedMix: Mixed supervised fed-erated learning for medical image segmentation. IEEE Transactions on Medical Imaging, 42(7), 1955–1968.
Williamson, S. M., & Prybutok, V. (2024). Balancing privacy and progress: A review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Applied Sciences, 14(2), 675.
Wu, C., Wu, F., Lyu, L., Huang, Y., & Xie, X. (2022). Communication-efficient federated learning via knowledge distil-lation. Nature Communications, 13(1), 2032.
Wu, Q., Chen, X., Zhou, Z., & Zhang, J. (2022). FedHome: Cloud-edge based personalized federated learning for in-home health monitoring. IEEE Transactions on Mobile Computing, 21(8), 2818–2832.
Xiao, X., Yin, J., Xu, J., Tat, T., & Chen, J. (2024). Advances in machine learning for wearable sensors. ACS Nano, 18(34), 22734–22751.
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated learning for healthcare informat-ics. Journal of Healthcare Informatics Research, 5(1), 1–19.
Yan, R., Qu, L., Wei, Q., Huang, S.-C., Shen, L., Rubin, D. L., Xing, L., & Zhou, Y. (2023). Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging, 42(7), 1932–1943.
Yan, Z., Wicaksana, J., Wang, Z., Yang, X., & Cheng, K.-T. (2021). Variation-aware federated learning with multi-source decentralized medical image data. IEEE Journal of Biomedical and Health Informatics, 25(7), 2615–2628.
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., Zhu, W., Carrafiello, G., Patella, F., Cariati, M., Obinata, H., Mori, H., Tamura, K., An, P., Wood, B. J., & Xu, D. (2021). Fed-erated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan. Medical Image Analysis, 70, 101992.
Yang, Z., Shi, Y., Zhou, Y., Wang, Z., & Yang, K. (2023). Trustworthy federated learning via blockchain. IEEE Internet of Things Journal, 10(1), 92–109.
Yaqoob, M. M., Nazir, M., Khan, M. A., Qureshi, S., & Al-Rasheed, A. (2023). Hybrid classifier-based federated learn-ing in health service providers for cardiovascular disease prediction. Applied Sciences, 13(3), 1911.
Yazdinejad, A., Dehghantanha, A., & Srivastava, G. (2024). AP2FL: Auditable privacy-preserving federated learning framework for electronics in healthcare. IEEE Transactions on Consumer Electronics, 70(1), 2527–2535.
Yeganeh, Y., Farshad, A., Navab, N., & Albarqouni, S. (2020). Inverse distance aggregation for federated learning with non-IID data. Lecture Notes in Computer Science, 12444 LNCS, 150–159.
Yu, H., Chen, Z., Zhang, X., Chen, X., Zhuang, F., Xiong, H., & Cheng, X. (2023). FedHAR: Semi-supervised online learning for personalized federated human activity recognition. IEEE Transactions on Mobile Computing, 22(6), 3318–3332.
Yu, S., Chen, X., Zhou, Z., Gong, X., & Wu, D. (2021). When deep reinforcement learning meets federated learning: In-telligent multitimescale resource management for multiaccess edge computing in 5G ultradense network. IEEE In-ternet of Things Journal, 8(4), 2238–2251.
Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022). Shifting machine learning for healthcare from development to deploy-ment and from models to data. Nature Biomedical Engineering, 6(12), 1330–1345.
Zhang, L., Xu, J., Vijayakumar, P., Sharma, P. K., & Ghosh, U. (2023). Homomorphic encryption-based privacy-preserving federated learning in IoT-enabled healthcare system. IEEE Transactions on Network Science and Engi-neering, 10(5), 2864–2880.
Zhang, W., Zhou, T., Lu, Q., Wang, X., Zhu, C., Sun, H., Wang, Z., Lo, S. K., & Wang, F.-Y. (2021). Dynamic-fusion-based federated learning for COVID-19 detection. IEEE Internet of Things Journal, 8(21), 15884–15891.
Zhou, X., Huang, W., Liang, W., Yan, Z., Ma, J., Pan, Y., & I-Kai Wang, K. (2024). Federated distillation and block-chain empowered secure knowledge sharing for internet of medical things. Information Sciences, 662, 120217.
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

📚 Journal: Healthcare Engineering | 📅 Year: 2026 | 📖 Volume: 2 | 📄 Issue: 4 | 👁️ Views: 238 | 📊 Crossref: 1

Related Articles:
  • Digital twins in healthcare: A scientometric analysis of virtual patient models, personalized medicine, and surgical applications based on highly cited publications (2000–2025)
  • Neural technology and human augmentation: A bibliometric analysis of research evolution, emerging paradigms, and future trajectories based on 200 highly cited publications (2009–2025)
  • A survey of the methods, aspects and trends of life insurance efficiency papers
  • Unlocking project SDGs in furniture manufacturing: The mediating role of green innovation resilience
  • Inventory control of deteriorating items: A review

📝 Ready to share your research?

Healthcare Engineering is accepting new submissions for upcoming issues. Join our community of authors and publish your work with us.

✓ Open access
✓ Rigorous peer review
✓ Fast publication
📤 Submit Your Manuscript →

📖 Author Guidelines

® 2010-2026 GrowingScience.Com