{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:49:28Z","timestamp":1776844168792,"version":"3.51.2"},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":23,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-026-01274-3","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T16:08:09Z","timestamp":1774886889000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Federated Learning Frameworks for Privacy-preserving Cyber Threat Detection in Healthcare Systems"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7838-6546","authenticated-orcid":false,"given":"Anayo Chukwu","family":"Ikegwu","sequence":"first","affiliation":[]},{"given":"Uzoma Rita","family":"Alo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5196-764X","authenticated-orcid":false,"given":"Henry Friday","family":"Nweke","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5857-8169","authenticated-orcid":false,"given":"Deborah Uzoamaka","family":"Ebem","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"1274_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10791-025-09686-z","volume":"28","author":"AC Ikegwu","year":"2025","unstructured":"Ikegwu, A.C., Alo, U.R., Nweke, H.F.: Cyber threats in mobile healthcare applications: systematic review of enabling technologies, threat models, detection approaches, and future directions. Discover Comput. 28, 1\u201345 (2025). https:\/\/doi.org\/10.1007\/s10791-025-09686-z","journal-title":"Discover Comput."},{"key":"1274_CR2","unstructured":"Simple, C.: Telehealth vs Telemedicine vs mHealth, and How RPM Fits In. CareSimple Inc. (2025). https:\/\/caresimple.com\/telehealth-vs-telemedicine-vs-mhealth-and-how-rpm-fits-in\/. Accessed: 10 Feb 2025"},{"issue":"01","key":"1274_CR3","doi-asserted-by":"publisher","first-page":"98","DOI":"10.30564\/jeis.v7i1.10050","volume":"07","author":"AC Ikegwu","year":"2025","unstructured":"Ikegwu, A.C., Uzuegbu, V.C., Alo, U.R.: Review of Embedded Systems and Cyber Threat Intelligence for Enhancing Data Security in Mobile Health. J. Electron. Inform. Syst. 07(01), 98\u2013120 (2025). https:\/\/doi.org\/10.30564\/jeis.v7i1.10050","journal-title":"J. Electron. Inform. Syst."},{"key":"1274_CR4","doi-asserted-by":"publisher","unstructured":"Arefin, S., Simcox, M.: AI-Driven Solutions for Safeguarding Healthcare Data: Innovations in Cybersecurity. Int. Bus. Res. 17(6) (2024). https:\/\/doi.org\/10.5539\/ibr.v17n6p74","DOI":"10.5539\/ibr.v17n6p74"},{"key":"1274_CR5","doi-asserted-by":"publisher","unstructured":"Ali, S., Li, Q., Yousafzai, A.: Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey. Ad Hoc Netw. 152 (2024). https:\/\/doi.org\/10.1016\/j.adhoc.2023.103320","DOI":"10.1016\/j.adhoc.2023.103320"},{"issue":"3","key":"1274_CR6","doi-asserted-by":"publisher","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","volume":"22","author":"WYB Lim","year":"2020","unstructured":"Lim, W.Y.B., et al.: Federated Learning in Mobile Edge Networks: A Comprehensive Survey. IEEE Commun. Surv. Tutorials. 22(3), 2031\u20132063 (2020). https:\/\/doi.org\/10.1109\/COMST.2020.2986024","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"1274_CR7","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3233\/SHTI220436","volume":"294","author":"K Narmadha","year":"2022","unstructured":"Narmadha, K., Varalakshmi, P.: Federated Learning in Healthcare: A Privacy Preserving Approach. Stud. Health Technol. Inf. 294, 194\u2013198 (2022). https:\/\/doi.org\/10.3233\/SHTI220436","journal-title":"Stud. Health Technol. Inf."},{"issue":"1","key":"1274_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-95858-2","volume":"15","author":"S Shukla","year":"2025","unstructured":"Shukla, S., Rajkumar, S., Sinha, A., Esha, M., Elango, K., Sampath, V.: Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity. Sci. Rep. 15(1), 1\u201333 (2025). https:\/\/doi.org\/10.1038\/s41598-025-95858-2","journal-title":"Sci. Rep."},{"key":"1274_CR9","unstructured":"Steve, A.: HIPAA Compliance for Medical Software Applications"},{"key":"1274_CR10","unstructured":"HIPAA, HIPAA Privacy and Security Rules. (2023)"},{"key":"1274_CR11","unstructured":"GDPR, General Data Protection Regulation (GDPR): Guidelines for Compliance (2022)"},{"key":"1274_CR12","doi-asserted-by":"publisher","first-page":"100068","DOI":"10.1016\/j.csa.2024.100068","volume":"3","author":"B Olanrewaju-George","year":"2025","unstructured":"Olanrewaju-George, B., Pranggono, B.: Federated learning-based intrusion detection system for the internet of things using unsupervised and supervised deep learning models. Cyber Secur. Appl. 3, 100068 (2025). https:\/\/doi.org\/10.1016\/j.csa.2024.100068","journal-title":"Cyber Secur. Appl."},{"key":"1274_CR13","doi-asserted-by":"publisher","unstructured":"Nasir, A.J., Chen, H., Hongsong, C.: Federated Learning Incentivize with Privacy-preserving for IoT in Edge Computing in the Context of B5G. Research Square. (2024). https:\/\/doi.org\/10.21203\/rs.3.rs-4513308\/v1","DOI":"10.21203\/rs.3.rs-4513308\/v1"},{"issue":"4","key":"1274_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijsea.2022.13401","volume":"13","author":"B Dash","year":"2022","unstructured":"Dash, B., Sharma, P., Ali, A.: Federated Learning for Privacy-Preserving: A Review of PII Data Analysis in Fintech. Int. J. Softw. Eng. Appl. 13(4), 1\u201313 (2022). https:\/\/doi.org\/10.5121\/ijsea.2022.13401","journal-title":"Int. J. Softw. Eng. Appl."},{"issue":"1","key":"1274_CR15","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s10207-023-00748-1","volume":"23","author":"OBJ Rabie","year":"2024","unstructured":"Rabie, O.B.J., Selvarajan, S., Hasanin, T., Mohammed, G.B., Alshareef, A.M., Uddin, M.: A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs). Int. J. Inf. Secur. 23(1), 51\u201380 (2024). https:\/\/doi.org\/10.1007\/s10207-023-00748-1","journal-title":"Int. J. Inf. Secur."},{"key":"1274_CR16","doi-asserted-by":"publisher","unstructured":"Pan, Y., Chao, Z., He, W., Jing, Y., Hongjia, L., Liming, W.: FedSHE: privacy preserving and efficient federated learning with adaptive segmented CKKS homomorphic encryption. Cybersecurity. 7(1) (2024). https:\/\/doi.org\/10.1186\/s42400-024-00232-w","DOI":"10.1186\/s42400-024-00232-w"},{"key":"1274_CR17","doi-asserted-by":"publisher","unstructured":"Adnan, M., Syed, M.H., Anjum, A., Rehman, S.: A Framework for privacy-preserving in IoV using Federated Learning with Differential Privacy. IEEE Access. pp. 13507\u201313521 (2025). https:\/\/doi.org\/10.1109\/ACCESS.2025.3526934","DOI":"10.1109\/ACCESS.2025.3526934"},{"key":"1274_CR18","doi-asserted-by":"publisher","unstructured":"Yang, M., Huang, D., Zhan, X.: Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development. (2024). https:\/\/doi.org\/10.20944\/preprints202410.1641.v1","DOI":"10.20944\/preprints202410.1641.v1"},{"key":"1274_CR19","doi-asserted-by":"publisher","unstructured":"Ghayoomi, H., Laskey, K., Miller-Hooks, E., Hooks, C., Tariverdi, M.: Assessing resilience of hospitals to cyberattack. Digit. Health. 7 (2021). https:\/\/doi.org\/10.1177\/20552076211059366","DOI":"10.1177\/20552076211059366"},{"key":"1274_CR20","doi-asserted-by":"publisher","first-page":"103754","DOI":"10.1016\/j.cose.2024.103754","volume":"140","author":"H Thanh","year":"2024","unstructured":"Thanh, H., et al.: Agriculture 4. 0 and beyond : Evaluating cyber threat intelligence sources and techniques in smart farming ecosystems. Comput. Secur. 140, 103754 (2024). https:\/\/doi.org\/10.1016\/j.cose.2024.103754","journal-title":"Comput. Secur."},{"issue":"8","key":"1274_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s19081788","volume":"19","author":"N Tariq","year":"2019","unstructured":"Tariq, N., et al.: The security of big data in fog-enabled iot applications including blockchain: A survey. Sens. (Switzerland). 19(8), 1\u201333 (2019). https:\/\/doi.org\/10.3390\/s19081788","journal-title":"Sens. (Switzerland)"},{"key":"1274_CR22","doi-asserted-by":"publisher","unstructured":"Pandey, M.K., Kar, N.K., Gupta, P.: Federated Learning: Introduction, Evolution, Working, Advantages, and Its Application in Various Domains. Model. Optim. Methods Efficient Edge AI: Federated Learn. Architectures Frameworks Appl. 109\u2013126 (2025). https:\/\/doi.org\/10.1002\/9781394219230.ch6","DOI":"10.1002\/9781394219230.ch6"},{"key":"1274_CR23","unstructured":"GDPR: General Data Protection Regulation (GDPR): Guidelines for Compliance. (2022). https:\/\/ec.europa.eu"},{"issue":"3","key":"1274_CR24","doi-asserted-by":"publisher","first-page":"1748","DOI":"10.1109\/COMST.2023.3273282","volume":"25","author":"N Sun","year":"2023","unstructured":"Sun, N., et al.: Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives. IEEE Commun. Surv. Tutorials. 25(3), 1748\u20131774 (2023). https:\/\/doi.org\/10.1109\/COMST.2023.3273282","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"1274_CR25","doi-asserted-by":"publisher","unstructured":"Duary, S., Choudhury, P., Mishra, S., Sharma, V., Rao, D.D., Aderemi, A.P.: Cybersecurity Threats Detection in Intelligent Networks using Predictive Analytics Approaches. In: 4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024. pp. 1\u20136 (2024). https:\/\/doi.org\/10.1109\/ICIPTM59628.2024.10563348","DOI":"10.1109\/ICIPTM59628.2024.10563348"},{"issue":"1","key":"1274_CR26","doi-asserted-by":"publisher","first-page":"179","DOI":"10.32996\/jcsts.2024.6.1.19","volume":"6","author":"MR Labu","year":"2024","unstructured":"Labu, M.R., Ahammed, M.F.: Next-Generation Cyber Threat Detection and Mitigation Strategies: A Focus on Artificial Intelligence and Machine Learning. J. Comput. Sci. Technol. Stud. 6(1), 179\u2013188 (2024). https:\/\/doi.org\/10.32996\/jcsts.2024.6.1.19","journal-title":"J. Comput. Sci. Technol. Stud."},{"key":"1274_CR27","doi-asserted-by":"publisher","first-page":"100206","DOI":"10.1016\/j.dajour.2023.100206","volume":"7","author":"AK Dey","year":"2023","unstructured":"Dey, A.K., Gupta, G.P., Sahu, S.P.: A metaheuristic-based ensemble feature selection framework for cyber threat detection in IoT-enabled networks. Decis. Analytics J. 7, 100206 (2023). https:\/\/doi.org\/10.1016\/j.dajour.2023.100206","journal-title":"Decis. Analytics J."},{"issue":"2","key":"1274_CR28","first-page":"1","volume":"3","author":"SS Balantrapu","year":"2024","unstructured":"Balantrapu, S.S.: Current Trends and Future Directions Exploring Machine Learning Techniques for Cyber Threat Detection. Int. J. Sustainable Dev. Through AI. 3(2), 1\u201315 (2024). ML and IoT","journal-title":"Int. J. Sustainable Dev. Through AI"},{"key":"1274_CR29","doi-asserted-by":"publisher","first-page":"23733","DOI":"10.1109\/ACCESS.2024.3363469","volume":"12","author":"MA Ferrag","year":"2024","unstructured":"Ferrag, M.A., et al.: Revolutionizing Cyber Threat Detection with Large Language Models: A Privacy-Preserving BERT-Based Lightweight Model for IoT\/IIoT Devices. IEEE Access. 12, 23733\u201323750 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3363469","journal-title":"IEEE Access."},{"issue":"1","key":"1274_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-69250-1","volume":"10","author":"MJ Sheller","year":"2020","unstructured":"Sheller, M.J., et al.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10(1), 1\u201312 (2020). https:\/\/doi.org\/10.1038\/s41598-020-69250-1","journal-title":"Sci. Rep."},{"key":"1274_CR31","first-page":"1","volume":"8","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Thing, V.L.L.: Three Decades of Deception Techniques in Active Cyber Defense - Retrospect and Outlook. arXiv:2104 03594v1 [cs CR]. 8, 1\u201319 (2021)","journal-title":"arXiv:2104 03594v1 [cs CR]"},{"key":"1274_CR32","doi-asserted-by":"publisher","unstructured":"Kairouz, P., et al.: Advances and open problems in federated learning. 14 (2021). https:\/\/doi.org\/10.1561\/2200000083","DOI":"10.1561\/2200000083"},{"key":"1274_CR33","doi-asserted-by":"publisher","unstructured":"Joshi, M., Pal, A., Sankarasubbu, M.: Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges. ACM Trans. Comput. Healthc. 3(4) (2022). https:\/\/doi.org\/10.1145\/3533708","DOI":"10.1145\/3533708"},{"issue":"1","key":"1274_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41666-020-00082-4","volume":"5","author":"J Xu","year":"2021","unstructured":"Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J., Wang, F.: Federated Learning for Healthcare Informatics. J. Healthc. Inf. Res. 5(1), 1\u201319 (2021). https:\/\/doi.org\/10.1007\/s41666-020-00082-4","journal-title":"J. Healthc. Inf. Res."},{"key":"1274_CR35","first-page":"1","volume":"6","author":"X Xu","year":"2021","unstructured":"Xu, X., Peng, H., Sun, L., Bhuiyan, M.Z.A., Liu, L., He, L.: FedMood: Federated Learning on Mobile Health Data for Mood Detection. arXiv:2102 09342 [cs CY]. 6, 1\u20139 (2021)","journal-title":"arXiv:2102 09342 [cs CY]"},{"key":"1274_CR36","doi-asserted-by":"publisher","first-page":"106019","DOI":"10.1016\/j.compbiomed.2022.106019","volume":"150","author":"A Rehman","year":"2022","unstructured":"Rehman, A., Sagheer, A., Khan, M.A., Ghazal, T.M., Adnan, K.M., Mosavi, A.: A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique. Comput. Biol. Med. 150, 106019 (2022). https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106019","journal-title":"Comput. Biol. Med."},{"key":"1274_CR37","doi-asserted-by":"publisher","unstructured":"Fereidooni, H., Dmitrienko, A., Rieger, P., Miettinen, M., Sadeghi, A.R., Madlener, F.: FedCRI: Federated Mobile Cyber-Risk Intelligence, 29th Annual Network and Distributed System Security Symposium, NDSS 2022. (2022). https:\/\/doi.org\/10.14722\/ndss.2022.23153","DOI":"10.14722\/ndss.2022.23153"},{"key":"1274_CR38","doi-asserted-by":"publisher","first-page":"83562","DOI":"10.1109\/ACCESS.2023.3301162","volume":"11","author":"M Abaoud","year":"2023","unstructured":"Abaoud, M., Almuqrin, M.A., Khan, M.F.: Advancing Federated Learning Through Novel Mechanism for Privacy Preservation in Healthcare Applications. IEEE Access. 11, 83562\u201383579 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3301162","journal-title":"IEEE Access."},{"issue":"5","key":"1274_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-024-10774-7","volume":"57","author":"H Li","year":"2024","unstructured":"Li, H., Ge, L., Tian, L.: Survey: federated learning data security and privacy-preserving in edge-Internet of Things. Artif. Intell. Rev. 57(5), 1\u201338 (2024). https:\/\/doi.org\/10.1007\/s10462-024-10774-7","journal-title":"Artif. Intell. Rev."},{"key":"1274_CR40","doi-asserted-by":"publisher","unstructured":"Baligodugula, V.V., Amsaad, F.: Hardware-Aware Federated Learning: Optimizing Differential Privacy in Distributed Computing Architectures. Electron. (Switzerland). 14(6) (2025). https:\/\/doi.org\/10.3390\/electronics14061218","DOI":"10.3390\/electronics14061218"},{"issue":"9","key":"1274_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s25092847","volume":"25","author":"L Zheng","year":"2025","unstructured":"Zheng, L., et al.: Sensitivity-Aware Differential Privacy for Federated Medical Imaging. Sensors. 25(9), 1\u201322 (2025). https:\/\/doi.org\/10.3390\/s25092847","journal-title":"Sensors"},{"key":"1274_CR42","doi-asserted-by":"publisher","unstructured":"Mehmood, M.H., Iqbal Khan, M., Ibrahim, A.: Balancing Privacy and Accuracy: Federated Learning with Differential Privacy for Medical Image Data, Proceedings\u2009\u2013\u20092024 7th International Conference on Data Science and Information Technology, DSIT 2024. (2024). https:\/\/doi.org\/10.1109\/DSIT61374.2024.10880906","DOI":"10.1109\/DSIT61374.2024.10880906"},{"key":"1274_CR43","doi-asserted-by":"publisher","unstructured":"Shen, A., Francisco, L., Sen, S., Tewari, A.: Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks. J. Med. Internet Res. 25 (2023). https:\/\/doi.org\/10.2196\/43664","DOI":"10.2196\/43664"},{"key":"1274_CR44","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1016\/j.future.2024.07.035","volume":"161","author":"A Aminifar","year":"2024","unstructured":"Aminifar, A., Shokri, M., Aminifar, A.: Privacy-preserving edge federated learning for intelligent mobile-health systems. Future Generation Comput. Syst. 161, 625\u2013637 (2024). https:\/\/doi.org\/10.1016\/j.future.2024.07.035","journal-title":"Future Generation Comput. Syst."},{"key":"1274_CR45","doi-asserted-by":"publisher","unstructured":"Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. Proc. ACM Conf. Comput. Commun. Secur. 1175\u20131191 (2017). https:\/\/doi.org\/10.1145\/3133956.3133982","DOI":"10.1145\/3133956.3133982"},{"key":"1274_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2024\/8138644","volume":"2024","author":"X Yang","year":"2024","unstructured":"Yang, X., Xing, C.: Federated Medical Learning Framework Based on Blockchain and Homomorphic Encryption. Wirel. Commun. Mob. Comput. 2024, 1\u201315 (2024). https:\/\/doi.org\/10.1155\/2024\/8138644","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"1274_CR47","unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-Efficient Learning of Deep Networks from Decentralized Data, in Proceedings of the 20th International Conference on Artificial In- telligence and Statistics (AISTATS) 2017, p. 10 (2017)"},{"issue":"3","key":"1274_CR48","first-page":"1","volume":"23","author":"DC Nguyen","year":"2021","unstructured":"Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A.: Federated Learning for Internet of Things: A Comprehensive Survey. EEE Commun. Surv. Tutorials. 23(3), 1\u201337 (2021)","journal-title":"EEE Commun. Surv. Tutorials"},{"key":"1274_CR49","doi-asserted-by":"publisher","unstructured":"Hady, A.A., Ghubaish, A., Salman, T., Unal, D., Jain, R.: Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study, IEEE Access. 8,106576\u2013106584 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3000421","DOI":"10.1109\/ACCESS.2020.3000421"},{"key":"1274_CR50","doi-asserted-by":"publisher","first-page":"165607","DOI":"10.1109\/ACCESS.2019.2953095","volume":"7","author":"J Lee","year":"2019","unstructured":"Lee, J., Kim, J., Kim, I., Han, K.: Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles. IEEE Access. 7, 165607\u2013165626 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2953095","journal-title":"IEEE Access."},{"issue":"1","key":"1274_CR51","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1038\/s41598-025-85866-7","volume":"15","author":"U Ahmed","year":"2025","unstructured":"Ahmed, U., et al.: Signature-based intrusion detection using machine learning and deep learning approaches empowered with fuzzy clustering. Sci. Rep. 15(1), 1726 (2025). https:\/\/doi.org\/10.1038\/s41598-025-85866-7","journal-title":"Sci. Rep."},{"key":"1274_CR52","doi-asserted-by":"publisher","unstructured":"Wang, C., Yao, C., Zhao, S., Zhao, S., Li, Y.: A Comparative Study of a Fully-Connected Artificial Neural Network and a Convolutional Neural Network in Predicting Bridge Maintenance Costs. Appl. Sci. (Switzerland). 12(7) (2022). https:\/\/doi.org\/10.3390\/app12073595","DOI":"10.3390\/app12073595"},{"key":"1274_CR53","unstructured":"Verma, D.: Enhancing Cybersecurity Through Adaptive Anomaly Detection Using Modern AI Techniques. Master\u2019s thesis of artificial intelligence. Department Math. Inform. Technol. pp. 1\u201370 (2024)"},{"key":"1274_CR54","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.iotcps.2023.09.003","volume":"4","author":"A Aldhaheri","year":"2024","unstructured":"Aldhaheri, A., Alwahedi, F., Ferrag, M.A., Battah, A.: Deep learning for cyber threat detection in IoT networks: A review. Internet Things Cyber-Physical Syst. 4, 110\u2013128 (2024). https:\/\/doi.org\/10.1016\/j.iotcps.2023.09.003","journal-title":"Internet Things Cyber-Physical Syst."},{"issue":"1","key":"1274_CR55","doi-asserted-by":"publisher","first-page":"301","DOI":"10.32604\/jai.2024.054314","volume":"6","author":"T Perumal","year":"2024","unstructured":"Perumal, T., Mustapha, N., Mohamed, R., Shiri, F.M.: Overview and Comparative Analysis on Deep Learning Models. J. Artif. Intell. 6(1), 301\u2013360 (2024). https:\/\/doi.org\/10.32604\/jai.2024.054314","journal-title":"J. Artif. Intell."},{"issue":"03","key":"1274_CR56","first-page":"20","volume":"01","author":"S Muhammad","year":"2024","unstructured":"Muhammad, S., Mirjat, N.A.: Enhancing Cybersecurity with AI: From Anomaly Detection to Threat Mitigation. Bull. Eng. Sci. Technol. 01(03), 20\u201339 (2024)","journal-title":"Bull. Eng. Sci. Technol."},{"key":"1274_CR57","doi-asserted-by":"publisher","first-page":"5647","DOI":"10.1109\/TIFS.2025.3574959","volume":"20","author":"G Xu","year":"2025","unstructured":"Xu, G., Fan, X., Xu, S., Cao, Y., Chen, X., Shang, T., Yu, S.: Anonymity-enhanced sequential multi-signer ring signature for secure medical data sharing in IoMT. IEEE Trans. Inf. Forensics Secur. 20, 5647\u20135662 (2025). https:\/\/doi.org\/10.1109\/TIFS.2025.3574959","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1274_CR58","doi-asserted-by":"publisher","unstructured":"Ding, F., Liu, Z., Wang, Y., Liu, J., Wei, C., Nguyen, A., Wang, N.: Intelligent event-triggered lane keeping security control for autonomous vehicle under DoS attacks. IEEE Trans. Fuzzy Syst. 1\u201313 (2025). https:\/\/doi.org\/10.1109\/TFUZZ.2025.3597276","DOI":"10.1109\/TFUZZ.2025.3597276"},{"key":"1274_CR59","doi-asserted-by":"publisher","unstructured":"Du, X., Zhu, J., Zhou, J., Pun, C., Lin, Z., Wu, C., Luo, J.: DP-TRAE: A dual-phase merging transferable reversible adversarial example for image privacy protection. IEEE Trans. Dependable Secur. Comput. 1\u201313 (2025). https:\/\/doi.org\/10.1109\/TDSC.2025.3601175","DOI":"10.1109\/TDSC.2025.3601175"},{"key":"1274_CR60","doi-asserted-by":"publisher","unstructured":"Xu, G., Lei, L., Mao, Y., Li, Z., Chen, X., Zhang, K.: CBRFL: A framework for committee-based Byzantine-resilient federated learning. J. Netw. Comput. Appl. 238(Art 104165) (2025). https:\/\/doi.org\/10.1016\/j.jnca.2025.104165","DOI":"10.1016\/j.jnca.2025.104165"},{"issue":"10","key":"1274_CR61","doi-asserted-by":"publisher","first-page":"14297","DOI":"10.1109\/JIOT.2025.3525623","volume":"12","author":"J Jin","year":"2025","unstructured":"Jin, J., Wu, M., Ouyang, A., Li, K., Chen, C.: A novel dynamic hill cipher and its applications on medical IoT. IEEE Internet Things J. 12(10), 14297\u201314308 (2025). https:\/\/doi.org\/10.1109\/JIOT.2025.3525623","journal-title":"IEEE Internet Things J."},{"key":"1274_CR62","doi-asserted-by":"publisher","unstructured":"Kang, S., Jin, S., Mao, X., He, B., Wu, C.: CD4\u2009+\u2009T and CD8\u2009+\u2009T cells in uterus exhibit both selective dysfunction and residency signatures. J. Immunol. Res. 2024(Art 5582151) (2024). https:\/\/doi.org\/10.1155\/2024\/5582151","DOI":"10.1155\/2024\/5582151"},{"key":"1274_CR63","doi-asserted-by":"publisher","unstructured":"Jiang, H., Jiang, Z., Tang, W., Xie, Y., Wang, M., Huang, W., Ye, T.: RobustHealth: Non-interactive privacy-preserving system for heterogeneous mobile health diagnosis. IEEE Trans. Mob. Comput. 1\u201315 (2025). https:\/\/doi.org\/10.1109\/TMC.2025.3634422","DOI":"10.1109\/TMC.2025.3634422"},{"issue":"2","key":"1274_CR64","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/TCC.2025.3559346","volume":"13","author":"F Zhang","year":"2025","unstructured":"Zhang, F., Zhang, C., Guan, J., Zhou, Q., Chen, K., Zhang, X., Du, X.: Breaking the edge: Enabling efficient neural network inference on integrated edge devices. IEEE Trans. Cloud Comput. 13(2), 694\u2013710 (2025). https:\/\/doi.org\/10.1109\/TCC.2025.3559346","journal-title":"IEEE Trans. Cloud Comput."},{"key":"1274_CR65","doi-asserted-by":"publisher","unstructured":"Wang, S., Zhang, K., Liu, A.: Flat-lattice-CNN: A model for Chinese medical-named-entity recognition. PLoS One. 20 (2025). https:\/\/doi.org\/10.1371\/journal.pone.0331464","DOI":"10.1371\/journal.pone.0331464"},{"issue":"1","key":"1274_CR66","doi-asserted-by":"publisher","first-page":"149","DOI":"10.36922\/AJWEP025040017","volume":"22","author":"V Mhaske","year":"2025","unstructured":"Mhaske, V., Kumar, P.M.A.: Securing smart health in smart cities: Blockchain technology to secure electronic health data sharing. Asian J. Water Environ. Pollut. 22(1), 149\u2013165 (2025). https:\/\/doi.org\/10.36922\/AJWEP025040017","journal-title":"Asian J. Water Environ. Pollut."},{"issue":"16","key":"1274_CR67","doi-asserted-by":"publisher","first-page":"32366","DOI":"10.1109\/JIOT.2025.3576735","volume":"12","author":"W Xu","year":"2025","unstructured":"Xu, W., Deng, J., Yu, J., Mao, S., Li, Y., Peng, Z., Xiao, B.: Blockchain-based verifiable decentralized identity for intelligent flexible manufacturing. IEEE Internet Things J. 12(16), 32366\u201332378 (2025). https:\/\/doi.org\/10.1109\/JIOT.2025.3576735","journal-title":"IEEE Internet Things J."},{"key":"1274_CR68","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/j.ins.2023.01.020","volume":"629","author":"Z Zhao","year":"2023","unstructured":"Zhao, Z., Li, X., Luan, B., Jiang, W., Gao, W., Neelakandan, S.: Secure Internet of Things (IoT) using a novel Brooks\u2013Iyengar quantum Byzantine agreement-centered blockchain networking (BIQBA-BCN) model in smart healthcare. Inf. Sci. 629, 440\u2013455 (2023). https:\/\/doi.org\/10.1016\/j.ins.2023.01.020","journal-title":"Inf. Sci."},{"key":"1274_CR69","doi-asserted-by":"publisher","unstructured":"He, W., Tan, J., Wang, R., Liu, Z., Luo, X., Hu, H., Zhang, H.: A deep reinforcement learning approach to time delay differential game deception resource deployment. IEEE Trans. Dependable Secur. Comput. 1\u201316 (2025). https:\/\/doi.org\/10.1109\/TDSC.2025.3620151","DOI":"10.1109\/TDSC.2025.3620151"},{"key":"1274_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-34536-9","volume":"26","author":"VS Naresh","year":"2026","unstructured":"Naresh, V.S., Ayyappa, D.: Privacy-preserving federated credit risk models: Evaluating differential privacy and homomorphic encryption techniques. Sci. Rep. 26, 1\u201318 (2026). https:\/\/doi.org\/10.1038\/s41598-025-34536-9","journal-title":"Sci. Rep."},{"key":"1274_CR71","doi-asserted-by":"publisher","unstructured":"Xue, B., Zheng, Q., Li, Z., Wang, J., Mu, C., Yang, J., Li, X.: Perturbation defense ultra high-speed weak target recognition. Eng. Appl. Artif. Intell. 138(Art 109420) (2024). https:\/\/doi.org\/10.1016\/j.engappai.2024.109420","DOI":"10.1016\/j.engappai.2024.109420"},{"issue":"4","key":"1274_CR72","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1080\/19361610.2025.2518383","volume":"20","author":"DS Sisodiya","year":"2025","unstructured":"Sisodiya, D.S., Tiwari, R., Jain, P., Aditya, Y.: An AI-based cyber ranges to strengthen the cybersecurity of cyber physical systems. J. Appl. Secur. Res. 20(4), 473\u2013505 (2025). https:\/\/doi.org\/10.1080\/19361610.2025.2518383","journal-title":"J. Appl. Secur. Res."},{"key":"1274_CR73","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1007\/s40998-025-00858-7","volume":"49","author":"Y Aditya","year":"2025","unstructured":"Aditya, Y., Jain, P., Tiwari, R.: Improved health care monitoring system using proposed LRRN-DLNN algorithm based on CPPFFR AI data preservation and ESRCC-based security. Iran. J. Sci. Technol. Trans. Electr. Eng. 49, 1771\u20131791 (2025). https:\/\/doi.org\/10.1007\/s40998-025-00858-7","journal-title":"Iran. J. Sci. Technol. Trans. Electr. Eng."},{"key":"1274_CR74","doi-asserted-by":"publisher","unstructured":"Aditya, Y., Jain, P., Tiwari, R.: Explainable AI framework for proactive cybersecurity defense. J. Comput. Inform. Syst. 1\u201314 (2025). https:\/\/doi.org\/10.1080\/08874417.2025.2579529","DOI":"10.1080\/08874417.2025.2579529"},{"key":"1274_CR75","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TP.2025.3650339","volume":"3","author":"P Jain","year":"2026","unstructured":"Jain, P., Aditya, Y., Rakesh, K., Gyanchandani, M.: Enhanced segment tree approach for multi-attribute numerical data in k-anonymization for data privacy. IEEE Trans. Priv. 3, 1\u201312 (2026). https:\/\/doi.org\/10.1109\/TP.2025.3650339","journal-title":"IEEE Trans. Priv."},{"issue":"5","key":"1274_CR76","doi-asserted-by":"publisher","first-page":"6256","DOI":"10.1109\/TAES.2024.3408139","volume":"60","author":"B Xue","year":"2024","unstructured":"Xue, B., Zheng, Q., Li, Z., Wang, J., Mu, C., Yang, J., Li, X.: ISAR weak feature enhancement with perturbation defense using hybrid clustering oversegmentation. IEEE Trans. Aerosp. Electron. Syst. 60(5), 6256\u20136274 (2024). https:\/\/doi.org\/10.1109\/TAES.2024.3408139","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"1274_CR77","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1109\/TNSRE.2023.3346955","volume":"32","author":"S Shi","year":"2024","unstructured":"Shi, S., Liu, W.: B2-ViT Net: Broad vision transformer network with broad attention for seizure prediction. IEEE Trans. Neural Syst. Rehabil. Eng. 32, 178\u2013188 (2024). https:\/\/doi.org\/10.1109\/TNSRE.2023.3346955","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"1274_CR78","doi-asserted-by":"publisher","unstructured":"Feng, X., Xin, R., Wu, J., Zheng, J., Wang, C., Yu, C.: AutoFE-Pointer: Auto-weighted feature extractor based on pointer network for DNA methylation prediction, International Journal of Biological Macromolecules. 311 (2025). https:\/\/doi.org\/10.1016\/j.ijbiomac.2025.143668","DOI":"10.1016\/j.ijbiomac.2025.143668"},{"issue":"9","key":"1274_CR79","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1007\/s00011-020-01368-4","volume":"69","author":"L Tang","year":"2020","unstructured":"Tang, L., Chen, Y., Xiang, Q., Xiang, J., Tang, Y., Li, J.: The association between IL18, FOXP3 and IL13 genes polymorphisms and risk of allergic rhinitis: A meta-analysis. Inflamm. Res. 69(9), 911\u2013923 (2020). https:\/\/doi.org\/10.1007\/s00011-020-01368-4","journal-title":"Inflamm. Res."},{"key":"1274_CR80","doi-asserted-by":"publisher","unstructured":"Yang, X., Chen, M., Cao, L., Zhao, M.: Bibliometric analysis of scientific papers on adverse reactions to COVID-19 vaccines published between 2019 and 2023, Human Vaccines & Immunotherapeutics. 19 (2023). https:\/\/doi.org\/10.1080\/21645515.2023.2270194","DOI":"10.1080\/21645515.2023.2270194"},{"key":"1274_CR81","doi-asserted-by":"publisher","DOI":"10.1109\/ASYU67174.2025.11208434","author":"AO Onlu","year":"2025","unstructured":"Onlu, A.O., Akca, B., Buyuktanir, B., Yildiz, K., Baydogmus, G.K.: An Investigation and Performance Evaluation of Aggregation Algorithms in Federated Learning Architecture. 2025 Innovations Intell. Syst. Appl. Conf. ASYU 2025. (2025). https:\/\/doi.org\/10.1109\/ASYU67174.2025.11208434","journal-title":"2025 Innovations Intell. Syst. Appl. Conf. ASYU 2025"},{"key":"1274_CR82","unstructured":"Devkota, A., Thrasher, J., Adjeroh, D., Bhattarai, B., Gyawali, P.K.: FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning. (2026). http:\/\/arxiv.org\/abs\/2602.21399"},{"key":"1274_CR83","doi-asserted-by":"publisher","first-page":"15867","DOI":"10.1109\/ACCESS.2024.3357514","volume":"12","author":"DN Sachin","year":"2024","unstructured":"Sachin, D.N., Annappa, B., Hegde, S., Abhijit, C.S., Ambesange, S.: FedCure: A Heterogeneity-Aware Personalized Federated Learning Framework for Intelligent Healthcare Applications in IoMT Environments. IEEE Access. 12, 15867\u201315883 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3357514","journal-title":"IEEE Access."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-026-01274-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01274-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01274-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T06:35:57Z","timestamp":1776839757000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-026-01274-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,30]]},"references-count":83,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1274"],"URL":"https:\/\/doi.org\/10.1007\/s44196-026-01274-3","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,30]]},"assertion":[{"value":"19 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest, both financial and non-financial, for this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"169"}}