A Narrative Review of Privacy Preserving Artificial Intelligence in Nursing Practice Through Federated Learning
DOI:
https://doi.org/10.35960/vm.v18i3.2226Keywords:
federated learning, nursing informatics, data privacy, clinical decision support systems, distributed artificial intelligenceAbstract
The rapid integration of artificial intelligence in nursing practice has enhanced predictive analytics, clinical decision support, and workforce management. However, concerns regarding data privacy, data silo fragmentation, and limited model generalizability remain significant challenges. Federated learning has emerged as a privacy preserving distributed machine learning approach that enables collaborative model development without transferring raw patient data across institutions. This narrative review aims to examine the conceptual foundation of federated learning and analyze its relevance for nursing practice and research. A literature search was conducted using Scopus and ScienceDirect databases covering publications from 2015 to 2025. Articles were analyzed through thematic synthesis focusing on technical architecture, clinical applications, ethical implications, and implementation challenges. The review indicates that federated learning has substantial potential to support predictive risk modeling, multicenter nursing outcome research, and integration within clinical decision support systems while maintaining patient confidentiality. Nevertheless, challenges related to non identical data distribution, governance accountability, interoperability, and digital literacy among nurses must be addressed to ensure safe and equitable implementation. Federated learning represents a strategic pathway for developing collaborative and privacy conscious artificial intelligence in nursing, provided that ethical safeguards, standardized data frameworks, and institutional readiness are systematically strengthened.
References
Abuhammad, S. (2025). Strengthening Ethical Practices of Patient Data Confidentiality and Sharing Among Nurses in the Artificial Intelligence-Driven Healthcare Era. Sage Open Nursing, 11. https://doi.org/10.1177/23779608251398113
Adamuz, J., Juvé-Udina, M.-E., González-Samartino, M., Jiménez-Martínez, E., Tapia-Pérez, M., López-Jiménez, M.-M., Romero-Garcia, M., & Delgado-Hito, P. (2020). Care complexity individual factors associated with adverse events and in-hospital mortality. PLOS ONE, 15(7), e0236370. https://doi.org/10.1371/journal.pone.0236370
Alharbi, R., Nguyen, T., Yousefzadeh, N., & Aljohani, A. (2025). Questionable Fairness in Federated Learning. 2025 8th International Conference on Data Science and Machine Learning Applications (CDMA), 31–36. https://doi.org/10.1109/CDMA61895.2025.00011
Ankolekar, A., Eppings, L., Bottari, F., Pinho, I. F., Howard, K., Baker, R., Nan, Y., Xing, X., Walsh, S. L., Vos, W., Yang, G., & Lambin, P. (2024). Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness. Computational and Structural Biotechnology Journal, 24, 412–419. https://doi.org/10.1016/j.csbj.2024.05.014
Aslan, M., & Toros, E. (2025). Machine Learning in Optimising Nursing Care Delivery Models: An Empirical Analysis of Hospital Wards. Journal of Evaluation in Clinical Practice, 31(1). https://doi.org/10.1111/jep.70001
Aunger, J. A., Millar, R., Rafferty, A. M., & Mannion, R. (2022). Collaboration over competition? Regulatory reform and inter-organisational relations in the NHS amidst the COVID-19 pandemic: a qualitative study. BMC Health Services Research, 22(1), 640. https://doi.org/10.1186/s12913-022-08059-2
Chai, S., Gu, M., Zhou, J., Zhang, W., & Zeng, X. (2020). Iterative Optimization for Edge Federated Learning (pp. 15–26). https://doi.org/10.1007/978-981-33-4336-8_2
Cheraghi, R., Valizadeh, L., Zamanzadeh, V., Hassankhani, H., & Jafarzadeh, A. (2023). Clarification of ethical principle of the beneficence in nursing care: an integrative review. BMC Nursing, 22(1), 89. https://doi.org/10.1186/s12912-023-01246-4
Chik, W., & Gamper, F. (2024). Ethical considerations and legal issues relating to federated learning. In Federated Learning (pp. 369–391). Elsevier. https://doi.org/10.1016/B978-0-44-319037-7.00032-6
Comparcini, D., Simonetti, V., Tomietto, M., Pastore, F., Totaro, M., Ballerini, P., Trerotoli, P., Mikkonen, K., & Cicolini, G. (2025). The Relationship Between Nurses’ Digital Health Literacy and Their Educational Levels, Professional Roles, and Digital Attitudes: A Cluster Analysis Based on a Cross‐Sectional Study. Journal of Clinical Nursing, 34(7), 2885–2897. https://doi.org/10.1111/jocn.17484
De Groot, K., De Veer, A. J. E., Paans, W., & Francke, A. L. (2020). Use of electronic health records and standardized terminologies: A nationwide survey of nursing staff experiences. International Journal of Nursing Studies, 104, 103523. https://doi.org/10.1016/j.ijnurstu.2020.103523
Dharmaji, V., Tanwar, M., Majumder, S., Rafique, M. M., & Paul, A. K. (2024). Towards Pre-Training Data Evaluation for Client Selection in Federated Learning. 2024 IEEE 31st International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW), 177–178. https://doi.org/10.1109/HiPCW63042.2024.00066
D’Hauwers, R., & Walravens, N. (2022). Do You Trust Me? Value and Governance in Data Sharing Business Models (pp. 217–225). https://doi.org/10.1007/978-981-16-2377-6_22
Dodevski, Z., Drusany Starič, K., Madevska Bogdanova, A., & Trajkovikj, V. (2024). Enhancing Privacy of Clinical Decision Support Systems with Federated Learning. 2024 9th International Conference on Smart and Sustainable Technologies (SpliTech), 1–6. https://doi.org/10.23919/SpliTech61897.2024.10612538
Eden, R., Chukwudi, I., Bain, C., Barbieri, S., Callaway, L., de Jersey, S., George, Y., Gorse, A.-D., Lawley, M., Marendy, P., McPhail, S. M., Nguyen, A., Samadbeik, M., & Sullivan, C. (2025). A scoping review of the governance of federated learning in healthcare. Npj Digital Medicine, 8(1), 427. https://doi.org/10.1038/s41746-025-01836-3
Elgujja, A. A. (2022). Impact of Information Technology on Patient Confidentiality Rights. In Research Anthology on Securing Medical Systems and Records (pp. 788–810). IGI Global. https://doi.org/10.4018/978-1-6684-6311-6.ch037
El-Yafouri, R., & Klieb, L. (2025). A scoping review of electronic health records interoperability levels, expectations, approaches, and problems. Health Informatics Journal, 31(4). https://doi.org/10.1177/14604582251385986
Fennelly, O., Grogan, L., Reed, A., & Hardiker, N. R. (2021). Use of standardized terminologies in clinical practice: A scoping review. International Journal of Medical Informatics, 149, 104431. https://doi.org/10.1016/j.ijmedinf.2021.104431
Fittipaldi, G., Couto, R. S., & Costa, L. H. M. K. (2025). Exploring traffic pattern variability in vehicular federated learning. Computer Communications, 242, 108279. https://doi.org/10.1016/j.comcom.2025.108279
Galatzan, B. J., Judson, T., Earnest, H., & Littleton, C. B. (2025). Integrating Digital Health Literacy into Undergraduate Nursing Education. CIN: Computers, Informatics, Nursing. https://doi.org/10.1097/CIN.0000000000001402
Garst, S., Dekker, J., & Reinders, M. (2025). A comprehensive experimental comparison between federated and centralized learning. Database, 2025. https://doi.org/10.1093/database/baaf016
Ghimire, A. (2026). A call to action to close the global digital divide in nursing: Clinical nursing information systems and standardized terminologies in low and middle-income countries. Health Informatics Journal, 32(1). https://doi.org/10.1177/14604582251406986
Grataloup, A., & Kurpicz-Briki, M. (2024). A systematic survey on the application of federated learning in mental state detection and human activity recognition. Frontiers in Digital Health, 6. https://doi.org/10.3389/fdgth.2024.1495999
Hariyati, R. T., Handiyani, H., Wildani, A., Afriani, T., Nuraini, T., & Amiruddin, M. (2024). Disparate Digital Literacy Levels of Nursing Manager and Staff, Specifically in Nursing Informatics Competencies and Their Causes: A Cross-Sectional Study. Journal of Healthcare Leadership, Volume 16, 415–425. https://doi.org/10.2147/JHL.S470456
Hasselgård, A.-M., Stafseth, S. K., & Kirkevold, Ø. (2024). Nursing Workload Prediction for Upcoming Shifts: A Retrospective Observational Exploratory Study in the Postoperative and Intensive Care Unit. Journal of Nursing Management, 2024, 1–9. https://doi.org/10.1155/2024/9703289
Hulme, M. (2025). Contextualising Trust in School-to-School Collaboration: Reflections From Wales. Improving Schools. https://doi.org/10.1177/13654802251406812
Jedwab, R. M., Holzhauser, K., Raghunathan, K., Lord, Z. K. M., Duncan, S. P., Murray, M. A., Gogler, J., & Hovenga AM, E. J. S. (2024). Applicability and benefits of Standardised Nursing Terminology in Australia: A scoping review. Collegian, 31(6), 404–420. https://doi.org/10.1016/j.colegn.2024.10.001
Jiang, X., Qian, J., Gu, A., Ma, X., Jin, K., Zhang, X., & Song, Z. (2026). SynthFed: Privacy-preserving long-tail ophthalmic diagnosis via VQ-VAE and GPT-augmented federated learning. Biomedical Signal Processing and Control, 113, 109181. https://doi.org/10.1016/j.bspc.2025.109181
Joynab, N. S., Islam, M. N., Aliya, R. R., Hasan, A. S. M. R., Khan, N. I., & Sarker, I. H. (2024). A federated learning aided system for classifying cervical cancer using PAP-SMEAR images. Informatics in Medicine Unlocked, 47, 101496. https://doi.org/10.1016/j.imu.2024.101496
Kamalzadeh, H., Mastaneh, Z., & Abedini, S. (2025). Navigating ethics: Nurses’ experiences with hospital information systems in the digital age. Nursing Ethics. https://doi.org/10.1177/09697330251385030
Kanada, K., Takemoto, S., Nozaki, Y., & Yoshikawa, M. (2025). Security Evaluation of Differentially Private Federated Learning Against Model Inversion Attacks. 2025 17th International Conference on Computer and Automation Engineering (ICCAE), 192–195. https://doi.org/10.1109/ICCAE64891.2025.10980580
Kane, R. L., & Chung, K. C. (2020). Collaboration in Hand Surgery: Experiences From Silicone Arthroplasty in Rheumatoid Arthritis, Finger Replantation and Amputation Challenges in Assessing Impairment, Satisfaction, and Effectiveness, Wrist and Radius Injury Surgical Trial, and Surgery of the Ulnar Nerve. Journal of the American Academy of Orthopaedic Surgeons, 28(15), e670–e678. https://doi.org/10.5435/JAAOS-D-20-00102
Karami, M., & Karami, A. (2025). Harmony in federated learning: a comprehensive review of techniques to tackle heterogeneity and non-IID data. Cluster Computing, 28(9), 570. https://doi.org/10.1007/s10586-025-05250-y
Kaur, G., & Grewal, S. K. (2024). Aggregation techniques in wireless communication using federated learning: a survey. International Journal of Wireless and Mobile Computing, 26(2), 115–126. https://doi.org/10.1504/IJWMC.2024.137135
Kovačević, A. (2025). Federated Learning in Healthcare: Improving Collaboration and Privacy (pp. 463–475). https://doi.org/10.1007/978-981-96-3077-6_28
Lee, C., Kim, J., Son, J., Han, T., Hong, N., & Park, M. (2025). Enhancing care transitions for older patients: A big data-driven readmission prediction model for personalized discharge nursing care. Geriatric Nursing, 65, 103519. https://doi.org/10.1016/j.gerinurse.2025.103519
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. https://doi.org/10.1016/j.future.2023.02.021
Long, C. P., & Sitkin, S. B. (2023). Contradictions that erode institutional trust & opportunities for addressing them. Behavioral Science & Policy, 9(2), 1–6. https://doi.org/10.1177/23794607241256709
Lopez-Baron, E., Abbas, Q., Caporal, P., Agulnik, A., Attebery, J. E., Holloway, A., Kissoon, N. “Tex,” Mulgado-Aguas, C. I., Amegan-Aho, K., Majdalani, M., Ocampo, C., Pascal, H., Miller, E., Kanyamuhunga, A., Tekleab, A. M., Bacha, T., González-Dambrauskas, S., Bhutta, A. T., Kortz, T. B., … Remy, K. E. (2024). Challenges in institutional ethical review process and approval for international multicenter clinical studies in lower and middle-income countries: the case of PARITY study. Frontiers in Pediatrics, 12. https://doi.org/10.3389/fped.2024.1460377
Mamun, Q. (2025). Sensor networks and blockchain platforms for the future healthcare. In Sensor Networks for Smart Hospitals (pp. 155–216). Elsevier. https://doi.org/10.1016/B978-0-443-36370-2.00010-4
McIlwain, L., Baird, J., Baldwin, C., Pickering, G., Manathunga, C., & Smith, T. (2024). Understanding the complex power dynamics that shape collaboration and social learning in multi-stakeholder water governance. Ecology and Society, 29(3), art31. https://doi.org/10.5751/ES-15109-290331
McMahon, M., Plate, S., Herz, T., Brenner, G., Kleinknecht-Dolf, M., & Krauthammer, M. (2025). Development of a Data-Based Method for Predicting Nursing Workload in an Acute Care Hospital: Methodological Study. Journal of Medical Internet Research, 27, e66667–e66667. https://doi.org/10.2196/66667
Meredith, P., Saville, C., Dall’Ora, C., Weeks, T., Wierzbicki, S., & Griffiths, P. (2025). Estimating Nurse Workload Using a Predictive Model From Routine Hospital Data: Algorithm Development and Validation. JMIR Medical Informatics, 13, e71666–e71666. https://doi.org/10.2196/71666
Michalakopoulos, V., Sarantinopoulos, E., Sarmas, E., & Marinakis, V. (2024). Empowering federated learning techniques for privacy-preserving PV forecasting. Energy Reports, 12, 2244–2256. https://doi.org/10.1016/j.egyr.2024.08.033
Mohamed, H., & El-Gayar, O. (2022). A Framework for Model-Centric Cross-Silo Horizontal Federated Machine Learning. 28th Americas Conference on Information Systems, AMCIS 2022. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192562665&partnerID=40&md5=38c1a05e3ed7354baf53fe49f60dbe34
Monsen, K. A., Heermann, L., & Dunn-Lopez, K. (2023). FHIR-up! Advancing knowledge from clinical data through application of standardized nursing terminologies within HL7® FHIR®. Journal of the American Medical Informatics Association, 30(11), 1858–1864. https://doi.org/10.1093/jamia/ocad131
Mukhtiar, N., Mahmood, A., & Sheng, Q. Z. (2025). Fairness in Federated Learning: Trends, Challenges, and Opportunities. Advanced Intelligent Systems, 7(6). https://doi.org/10.1002/aisy.202400836
Namdar Areshtanab, H., Rahmani, F., Vahidi, M., Saadati, S. Z., & Pourmahmood, A. (2025). Nurses perceptions and use of artificial intelligence in healthcare. Scientific Reports, 15(1), 27801. https://doi.org/10.1038/s41598-025-11002-0
Nikbakht Nasrabadi, A., Norouzkhani, N., Manookian, A., Cheraghi, M. A., Mohammadi, M., Izadidastenaei, Z., & Goudarzian, A. H. (2024). Safeguarding Patient Information as an Issue Faced by Nurses: A policy brief. Asia Pacific Journal of Health Management. https://doi.org/10.24083/apjhm.v19i2.3013
Obakhena, H. I., Imoize, A. L., & Anyasi, F. I. (2024). Integration of federated learning paradigms into electronic health record systems. In Federated Learning for Digital Healthcare Systems (pp. 203–236). Elsevier. https://doi.org/10.1016/B978-0-443-13897-3.00017-5
Oh, E. G., Oh, S., Cho, S., & Moon, M. (2025). Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study. JMIR Medical Informatics, 13, e56671–e56671. https://doi.org/10.2196/56671
Oh, W., & Nadkarni, G. N. (2023). Federated Learning in Health care Using Structured Medical Data. Advances in Kidney Disease and Health, 30(1), 4–16. https://doi.org/10.1053/j.akdh.2022.11.007
Oliveira, F., Morais, E. J., Cardoso, A., Brito, A., Gonçalves, P., Bastos, F., Machado, N., Cruz, I., Sousa, P., & Pereira, F. (2024). Cross-Mapping the Portuguese Nursing Ontology with ICNP, SNOMED CT and NANDA-I. https://doi.org/10.3233/SHTI240111
Ostberg, N., Ling, J., Winter, S. G., Som, S., Vasilakis, C., Shin, A. Y., Cornell, T. T., & Scheinker, D. (2021). Quantifying paediatric intensive care unit staffing levels at a paediatric academic medical centre: A mixed‐methods approach. Journal of Nursing Management, 29(7), 2278–2287. https://doi.org/10.1111/jonm.13346
Park, Y., Chang, S. J., & Kim, E. (2025). Artificial intelligence in critical care nursing: A scoping review. Australian Critical Care, 38(4), 101225. https://doi.org/10.1016/j.aucc.2025.101225
Pathak, J., & Rajput, A. S. (2024). Distributed information fusion for secure healthcare. In Data Fusion Techniques and Applications for Smart Healthcare (pp. 361–384). Elsevier. https://doi.org/10.1016/B978-0-44-313233-9.00022-9
Peregrina, J. A., Ortiz, G., & Zirpins, C. (2022). Towards a Metadata Management System for Provenance, Reproducibility and Accountability in Federated Machine Learning (pp. 5–18). https://doi.org/10.1007/978-3-031-23298-5_1
Ramirez, J. P., & Jenkins, K. (2024). Artificial intelligence for quality improvement. In Intelligence-Based Cardiology and Cardiac Surgery (pp. 321–325). Elsevier. https://doi.org/10.1016/B978-0-323-90534-3.00028-7
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. https://doi.org/10.1016/j.compbiomed.2022.106019
Romero-Brufau, S., Smunyahirun, R., Filhol, T., Niederhauser, L., Trakoolwilaiwan, T., & Singh, G. (2025). Vital Signs–Only Machine Learning Model for Acute Inpatient Deterioration: A Retrospective Multicenter Study. Mayo Clinic Proceedings: Innovations, Quality & Outcomes, 9(5), 100663. https://doi.org/10.1016/j.mayocpiqo.2025.100663
S N, P., Saini, D. K. J. B., Shelke, N., Pimpalkar, A., Kumar, G. H., & V, V. (2025). Privacy-Preserving and Scalable Secure Aggregation for Federated Learning in Edge Computing. 2025 Second International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS), 182–188. https://doi.org/10.1109/ICC-ROBINS64345.2025.11086126
Sacca, L., Lobaina, D., Burgoa, S., Lotharius, K., Moothedan, E., Gilmore, N., Xie, J., Mohler, R., Scharf, G., Knecht, M., & Kitsantas, P. (2024). Promoting Artificial Intelligence for Global Breast Cancer Risk Prediction and Screening in Adult Women: A Scoping Review. Journal of Clinical Medicine, 13(9), 2525. https://doi.org/10.3390/jcm13092525
Salles, A. A., & Castelo, L. (2023). Privacidade e confidencialidade nos processos terapêuticos: presença da fundamentação bioética. Revista Bioética, 31. https://doi.org/10.1590/1983-803420233340pt
Sanz, D., Ebert, M., Namias, M., & Jeraj, R. (2025). Building Global Inter-Institutional Collaborations. In Global Medical Physics (pp. 24–35). CRC Press. https://doi.org/10.1201/9781003527749-3
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 sharing. Journal of the American Medical Informatics Association, 28(6), 1259–1264. https://doi.org/10.1093/jamia/ocaa341
Shiri, F. M., Perumal, T., Mustapha, N., & Mohamed, R. (2025). Deep Learning and Federated Learning in Human Activity Recognition with Sensor Data: A Comprehensive Review. Computer Modeling in Engineering & Sciences, 145(2), 1389–1485. https://doi.org/10.32604/cmes.2025.071858
Silva, C. G. da, Vega, E. A. U., Cordova, F. P., Carneiro, F. A., Azzolin, K. de O., Rosso, L. H. de, Graeff, M. dos S., Carvalho, P. V. de, & Almeida, M. de A. (2020). SNOMED-CT as a standardized language system model for nursing: an integrative review. Revista Gaúcha de Enfermagem, 41. https://doi.org/10.1590/1983-1447.2020.20190281
Song, J., Gao, Y., Yin, P., Li, Yi, Li, Yang, Zhang, J., Su, Q., Fu, X., & Pi, H. (2021). The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms. Risk Management and Healthcare Policy, Volume 14, 1175–1187. https://doi.org/10.2147/RMHP.S297838
Song, W., Kang, M.-J., Zhang, L., Jung, W., Song, J., Bates, D. W., & Dykes, P. C. (2021). Predicting pressure injury using nursing assessment phenotypes and machine learning methods. Journal of the American Medical Informatics Association, 28(4), 759–765. https://doi.org/10.1093/jamia/ocaa336
Song, Y., Zhang, X., Luo, D., Shi, J., Zang, Q., Wang, Y., Yin, H., Xu, G., & Bai, Y. (2024). Predicting nursing workload in digestive wards based on machine learning: A prospective study. BMC Nursing, 23(1), 908. https://doi.org/10.1186/s12912-024-02570-z
Soni, N., Chowdhury, A., & Bansal, H. (2025). Collaborative Health Intelligence (pp. 111–148). https://doi.org/10.4018/979-8-3373-3306-9.ch005
Tuli, S., Mirhakimi, F., Pallewatta, S., Zawad, S., Casale, G., Javadi, B., Yan, F., Buyya, R., & Jennings, N. R. (2023). AI augmented Edge and Fog computing: Trends and challenges. Journal of Network and Computer Applications, 216, 103648. https://doi.org/10.1016/j.jnca.2023.103648
Tumulak, A., Tin, J., & Keshavjee, K. (2024). Towards a Unified Framework for Information and Interoperability Governance. https://doi.org/10.3233/SHTI231310
Upreti, D., Yang, E., Kim, H., & Seo, C. (2024). A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications. Computer Modeling in Engineering & Sciences, 140(3), 2239–2274. https://doi.org/10.32604/cmes.2024.048932
Varkey, B. (2021). Principles of Clinical Ethics and Their Application to Practice. Medical Principles and Practice, 30(1), 17–28. https://doi.org/10.1159/000509119
Verma, V. V., Samal, S., & Renuka, J. S. (2023). Exploring Health Professionals’ Perspectives on Patient Confidentiality and Contributing Factors in Resource-Limited Environments. Seminars in Medical Writing and Education, 2, 142. https://doi.org/10.56294/mw2023142
von Gerich, H., Moen, H., Block, L. J., Chu, C. H., DeForest, H., Hobensack, M., Michalowski, M., Mitchell, J., Nibber, R., Olalia, M. A., Pruinelli, L., Ronquillo, C. E., Topaz, M., & Peltonen, L.-M. (2022). Artificial Intelligence -based technologies in nursing: A scoping literature review of the evidence. International Journal of Nursing Studies, 127, 104153. https://doi.org/10.1016/j.ijnurstu.2021.104153
Wang, B., Tian, Z., Liu, X., Xia, Y., She, W., & Liu, W. (2025). A multi-center federated learning mechanism based on consortium blockchain for data secure sharing. Knowledge-Based Systems, 310, 112962. https://doi.org/10.1016/j.knosys.2025.112962
Wang, C., Xia, H., Xu, S., Chi, H., Zhang, R., & Hu, C. (2024). FedBnR: Mitigating federated learning Non-IID problem by breaking the skewed task and reconstructing representation. Future Generation Computer Systems, 153, 1–11. https://doi.org/10.1016/j.future.2023.11.020
Wang, X., Liang, Z., Koe, A. S. V., Wu, Q., Zhang, X., Li, H., & Yang, Q. (2022). Secure and efficient parameters aggregation protocol for federated incremental learning and its applications. International Journal of Intelligent Systems, 37(8), 4471–4487. https://doi.org/10.1002/int.22727
Wang, Y., Gao, Y., Zhang, Y., Geng, H., & Chi, H. (2026). Accountable federated learning against local poisoning attacks. Knowledge-Based Systems, 331, 114828. https://doi.org/10.1016/j.knosys.2025.114828
Wasser, T., & Sun, A. Y. (2024). Confidentiality and Privilege. In Psychiatry and the Law (pp. 19–32). Springer International Publishing. https://doi.org/10.1007/978-3-031-52589-6_3
Weston, F. C. L., Paglioli, A. C. B., & Mesquita, M. W. (2023). General Law on Personal Data Protection and applicability to Nursing. Revista Brasileira de Enfermagem, 76(suppl 3). https://doi.org/10.1590/0034-7167-2023-0126
Xin, W., Jiaqian, L., Xueshuang, D., Haoji, Z., & Lianshan, S. (2025). A Survey of Differential Privacy Techniques for Federated Learning. IEEE Access, 13, 6539–6555. https://doi.org/10.1109/ACCESS.2024.3523909
Xing, S., Du, X., Hu, Y., & Pu, Y. (2025). Time series analysis of outpatient blood collection visits: Fluctuation patterns and nursing staff allocation optimization. International Journal of Nursing Sciences, 12(5), 425–430. https://doi.org/10.1016/j.ijnss.2025.08.007
Xu, H., Feng, Y., & Xie, K. (2024). Verifiable Federated Learning Based on Data Service Quality. 2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS), 243–248. https://doi.org/10.1109/ISPDS62779.2024.10667494
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated Learning for Healthcare Informatics. Journal of Healthcare Informatics Research, 5(1), 1–19. https://doi.org/10.1007/s41666-020-00082-4
Yordanov, T. R., Ravelli, A. C. J., Amiri, S., Vis, M., Houterman, S., Van der Voort, S. R., & Abu-Hanna, A. (2024). Performance of federated learning-based models in the Dutch TAVI population was comparable to central strategies and outperformed local strategies. Frontiers in Cardiovascular Medicine, 11. https://doi.org/10.3389/fcvm.2024.1399138
Yurdem, B., Kuzlu, M., Gullu, M. K., Catak, F. O., & Tabassum, M. (2024). Federated learning: Overview, strategies, applications, tools and future directions. Heliyon, 10(19), e38137. https://doi.org/10.1016/j.heliyon.2024.e38137
Zhang, F., Shuai, Z., Kuang, K., Wu, F., Zhuang, Y., & Xiao, J. (2024). Unified fair federated learning for digital healthcare. Patterns, 5(1), 100907. https://doi.org/10.1016/j.patter.2023.100907
Zhang, H., Jiang, S., & Xuan, S. (2024). Decentralized federated learning based on blockchain: concepts, framework, and challenges. Computer Communications, 216, 140–150. https://doi.org/10.1016/j.comcom.2023.12.042
Zhou, X., Lin, Y., Hooimeijer, P., & Monstadt, J. (2025). Institutional Design of Collaborative Water Governance: The River Chief System in China. Environmental Policy and Governance, 35(3), 525–537. https://doi.org/10.1002/eet.2152
Zhu, S.-Y., Tang, W., Xie, Y.-T., & Xie, L.-Z. (2025). A comprehensive review of federated learning for multi-center medical data. Biomedical Engineering Communications, 4(2), 9. https://doi.org/10.53388/BMEC2025009
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Iis Setiawan Mangkunegara, Purwono Purwono (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submitted paper will be firstly reviewed by the editors to determine whether the paper meet the edition theme and submission guidelines. Papers which meet the theme and the guidelines will be assigned to selected reviewers for peer-reviews. Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan is a double blind peer-reviewed journal which involves reviewers based on their experties relevant to the topic of the paper. Final decision of paper acceptance is solely decided by the editors according to reviewers' comment.
Plagiarism and self-plagiarism are prohibited. Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan uses PlagiarismCheckerX and iThenticate to scan papers for detecting plagiarism. Thus, Appropriate citation and quotation should be used
.png)
