{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T12:23:21Z","timestamp":1761913401523,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032024886"},{"type":"electronic","value":"9783032024893"}],"license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-02489-3_21","type":"book-chapter","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T12:17:16Z","timestamp":1761913036000},"page":"277-289","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Privacy Preservation and\u00a0Reducing Analysis Time with\u00a0Federated Transfer Learning in\u00a0Digital Twins-Based Computed Tomography Scan Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0199-0984","authenticated-orcid":false,"given":"Avais","family":"Jan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2028-5960","authenticated-orcid":false,"given":"Qasim","family":"Zia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4329-0234","authenticated-orcid":false,"given":"Murray","family":"Patterson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","first-page":"5164970","DOI":"10.1155\/2022\/5164970","volume":"2022","author":"S Hussain","year":"2022","unstructured":"Hussain, S., et al.: Modern diagnostic imaging technique applications and risk factors in the medical field: a review. Biomed. Res. Int. 2022, 5164970 (2022)","journal-title":"Biomed. Res. Int."},{"key":"21_CR2","doi-asserted-by":"publisher","first-page":"4860","DOI":"10.3390\/electronics13244860","volume":"13","author":"M Shakor","year":"2024","unstructured":"Shakor, M., Khaleel, M.: Recent advances in big medical image data analysis through deep learning and cloud computing. Electronics 13, 4860 (2024)","journal-title":"Electronics"},{"key":"21_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3453476","volume":"55","author":"D Nguyen","year":"2022","unstructured":"Nguyen, D., Pham, Q., Pathirana, P., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., Hwang, W.: Federated learning for smart healthcare: a survey. ACM Comput. Surv. (Csur). 55, 1\u201337 (2022)","journal-title":"ACM Comput. Surv. (Csur)."},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Cirillo, F., De Santis, M., Esposito, C.: Applications of Solid Platform and Federated Learning for Decentralized Health Data Management. In:Artificial Intelligence Techniques For Analysing Sensitive Data In Medical Cyber-Physical Systems: System Protection And Data Analysis, pp. 95-111 (2025)","DOI":"10.1007\/978-3-031-70775-9_6"},{"key":"21_CR5","doi-asserted-by":"publisher","first-page":"11045","DOI":"10.1007\/s10489-022-04065-3","volume":"53","author":"S Dai","year":"2023","unstructured":"Dai, S., Meng, F.: Addressing modern and practical challenges in machine learning: a survey of online federated and transfer learning. Appl. Intell. 53, 11045\u201311072 (2023)","journal-title":"Appl. Intell."},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Subasi, A., Subasi, M.: Digital twins in healthcare and biomedicine. In: Artificial Intelligence, Big Data, Blockchain And 5G for the Digital Transformation of the Healthcare Industry, pp. 365\u2013401 (2024)","DOI":"10.1016\/B978-0-443-21598-8.00011-7"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Sheller, M., Reina, G., Edwards, B., Martin, J., Bakas, S.: Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke And Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held In Conjunction With MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4, pp. 92-104 (2019)","DOI":"10.1007\/978-3-030-11723-8_9"},{"key":"21_CR8","unstructured":"Zia, Q., Farooq, M., Abid, A.: Improving Response Time of Vehicular Ad hoc NETworks (VANET) (2016)"},{"key":"21_CR9","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.3390\/electronics14061084","volume":"14","author":"Q Zia","year":"2025","unstructured":"Zia, Q., Jan, A., Yang, D., Zhang, H., Li, Y.: Optimized real-time decision making with EfficientNet in digital twin-based vehicular networks. Electronics 14, 1084 (2025)","journal-title":"Electronics"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Dou, Q., et al.: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ Digital Med. 4, 60 (2021)","DOI":"10.1038\/s41746-021-00431-6"},{"key":"21_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112208","volume":"300","author":"Z Yin","year":"2024","unstructured":"Yin, Z., Wang, H., Chen, B., Zhang, X., Lin, X., Sun, H., Li, A., Zhou, C.: Federated semi-supervised representation augmentation with cross-institutional knowledge transfer for healthcare collaboration. Knowl.-Based Syst. 300, 112208 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Zia, Q., Wang, C., Zhu, S., Li, Y.: Priority based inter-twin communication in vehicular digital twin networks, pp. 1\u201316. Int. J. Parallel Emergent Distribut. Syst. (2024)","DOI":"10.1080\/17445760.2024.2398082"},{"key":"21_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102364","volume":"108","author":"M Irfan","year":"2024","unstructured":"Irfan, M., Malik, K., Muhammad, K.: Federated fusion learning with attention mechanism for multi-client medical image analysis. Inf. Fusion 108, 102364 (2024)","journal-title":"Inf. Fusion"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Zia, Q., Zhu, S., Wang, H., Iqbal, Z., Li, Y.: Hierarchical Federated Transfer Learning in digital twin-based vehicular networks. High-Confidence Comput., 100303 (2025)","DOI":"10.1016\/j.hcc.2025.100303"},{"key":"21_CR15","doi-asserted-by":"publisher","first-page":"3688","DOI":"10.1109\/JSAC.2021.3118352","volume":"39","author":"W Zhang","year":"2021","unstructured":"Zhang, W., Yang, D., Wu, W., Peng, H., Zhang, N., Zhang, H., Shen, X.: Optimizing federated learning in distributed industrial IoT: A multi-agent approach. IEEE J. Sel. Areas Commun. 39, 3688\u20133703 (2021)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"21_CR16","first-page":"127","volume":"8","author":"Q Zia","year":"2015","unstructured":"Zia, Q.: A survey of data-centric protocols for wireless sensor networks. Comput. Sci. Syst. Biol. OMICS Publishing Group. 8, 127\u2013131 (2015)","journal-title":"Comput. Sci. Syst. Biol. OMICS Publishing Group."},{"key":"21_CR17","doi-asserted-by":"publisher","first-page":"5918","DOI":"10.3390\/s22155918","volume":"22","author":"R Sahal","year":"2022","unstructured":"Sahal, R., Alsamhi, S., Brown, K.: Personal digital twin: a close look into the present and a step towards the future of personalised healthcare industry. Sensors. 22, 5918 (2022)","journal-title":"Sensors."},{"key":"21_CR18","first-page":"2022","volume":"13","author":"M Hany","year":"2020","unstructured":"Hany, M.: Chest CT-scan images dataset. Kaggle 13, 2022 (2020)","journal-title":"Kaggle"}],"container-title":["Lecture Notes in Computer Science","Computational Advances in Bio and Medical Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02489-3_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T12:17:23Z","timestamp":1761913043000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02489-3_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"ISBN":["9783032024886","9783032024893"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02489-3_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"1 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCABS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Advances in Bio and Medical Sciences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Atlanta, GA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 January 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 January 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccabs2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/iccabs-2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}