{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T13:05:31Z","timestamp":1784898331229,"version":"3.55.0"},"reference-count":86,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100002570","name":"Woosong University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002570","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.eswa.2026.131514","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:30:28Z","timestamp":1770395428000},"page":"131514","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Breaking barriers in federated transfer learning: A systematic review on federated transfer learning for non-overlapping domains"],"prefix":"10.1016","volume":"313","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9249-1625","authenticated-orcid":false,"given":"Tasnim Binte","family":"Shiraj","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4882-4327","authenticated-orcid":false,"given":"Sobhana","family":"Jahan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9357-8436","authenticated-orcid":false,"given":"Md. Rawnak Saif","family":"Adib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9178-3711","authenticated-orcid":false,"given":"Md. Sazzadur","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4604-5461","authenticated-orcid":false,"given":"M. Shamim","family":"Kaiser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0934-0995","authenticated-orcid":false,"given":"A. S. M. Sanwar","family":"Hosen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.131514_bib0001","unstructured":"Acar, D. A. E., Zhao, Y., Navarro, R. M., Mattina, M., Whatmough, P. N., & Saligrama, V. (2021). Federated learning based on dynamic regularization. arXiv preprint arXiv: 2111.04263."},{"issue":"5","key":"10.1016\/j.eswa.2026.131514_bib0002","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.3390\/s21051688","article-title":"Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures","volume":"21","author":"Ali","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.eswa.2026.131514_bib0003","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1016\/j.procs.2023.01.127","article-title":"Simulating federated transfer learning for lung segmentation using modified UNet model","volume":"218","author":"Ambesange","year":"2023","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.eswa.2026.131514_bib0004","series-title":"Ambient assisted living and home care: 4th international workshop, IWAAL 2012, Vitoria-Gasteiz, Spain, December 3\u20135, 2012. proceedings 4","first-page":"216","article-title":"Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine","author":"Anguita","year":"2012"},{"key":"10.1016\/j.eswa.2026.131514_bib0005","series-title":"Seventh IEEE international conference on data mining workshops (ICDMW 2007)","first-page":"77","article-title":"A comparative study of methods for transductive transfer learning","author":"Arnold","year":"2007"},{"issue":"6","key":"10.1016\/j.eswa.2026.131514_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.103061","article-title":"Federated learning review: Fundamentals, enabling technologies, and future applications","volume":"59","author":"Banabilah","year":"2022","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.eswa.2026.131514_bib0007","series-title":"Proceedings of ICML workshop on unsupervised and transfer learning","first-page":"17","article-title":"Deep learning of representations for unsupervised and transfer learning","author":"Bengio","year":"2012"},{"key":"10.1016\/j.eswa.2026.131514_bib0008","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ijmedinf.2018.01.007","article-title":"Federated learning of predictive models from federated electronic health records","volume":"112","author":"Brisimi","year":"2018","journal-title":"International Journal of Medical Informatics"},{"key":"10.1016\/j.eswa.2026.131514_bib0009","article-title":"Federated transfer learning with orchard-optimized conv-SGRU: A novel approach to secure and accurate photovoltaic power forecasting","volume":"48","author":"Bukhari","year":"2024","journal-title":"Renewable Energy Focus"},{"issue":"5","key":"10.1016\/j.eswa.2026.131514_bib0010","first-page":"24","article-title":"Google deepmind\u2019s alphago: Operations research\u2019s unheralded role in the path-breaking achievement","volume":"43","author":"Chang","year":"2016","journal-title":"Or\/Ms Today"},{"issue":"4","key":"10.1016\/j.eswa.2026.131514_bib0011","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MIS.2020.2988604","article-title":"Fedhealth: A federated transfer learning framework for wearable healthcare","volume":"35","author":"Chen","year":"2020","journal-title":"IEEE Intelligent Systems"},{"key":"10.1016\/j.eswa.2026.131514_bib0012","series-title":"Proceedings of the ACM international conference on image and video retrieval","article-title":"Nus-wide: A real-world web image database from national university of singapore","author":"Chua","year":"2009"},{"key":"10.1016\/j.eswa.2026.131514_bib0013","doi-asserted-by":"crossref","first-page":"10572","DOI":"10.52202\/068431-0768","article-title":"FedAvg with fine tuning: Local updates lead to representation learning","volume":"35","author":"Collins","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.131514_bib0014","unstructured":"Credit Fusion, W. C. (2011). Give me some credit. https:\/\/kaggle.com\/competitions\/GiveMeSomeCredit."},{"issue":"9","key":"10.1016\/j.eswa.2026.131514_bib0015","doi-asserted-by":"crossref","first-page":"11045","DOI":"10.1007\/s10489-022-04065-3","article-title":"Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning","volume":"53","author":"Dai","year":"2023","journal-title":"Applied Intelligence"},{"issue":"3","key":"10.1016\/j.eswa.2026.131514_bib0016","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.drudis.2020.12.003","article-title":"Advanced machine-learning techniques in drug discovery","volume":"26","author":"Elbadawi","year":"2021","journal-title":"Drug Discovery Today"},{"key":"10.1016\/j.eswa.2026.131514_bib0017","series-title":"Advances in data science and information engineering: proceedings from ICDATA 2020 and IKE 2020","first-page":"877","article-title":"A brief review of domain adaptation","author":"Farahani","year":"2021"},{"key":"10.1016\/j.eswa.2026.131514_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109384","article-title":"Semi-supervised federated heterogeneous transfer learning","volume":"252","author":"Feng","year":"2022","journal-title":"Knowledge-Based Systems"},{"issue":"2","key":"10.1016\/j.eswa.2026.131514_bib0019","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1016\/j.ifacol.2020.12.1888","article-title":"Early history of machine learning","volume":"53","author":"Fradkov","year":"2020","journal-title":"IFAC-PapersOnLine"},{"key":"10.1016\/j.eswa.2026.131514_bib0020","series-title":"Data","first-page":"17","article-title":"Asymmetric heterogeneous transfer learning: A survey","author":"Friedjungov\u00e1","year":"2017"},{"key":"10.1016\/j.eswa.2026.131514_bib0021","series-title":"2019\u202fIEEE International conference on big data (big data)","first-page":"2552","article-title":"Privacy-preserving heterogeneous federated transfer learning","author":"Gao","year":"2019"},{"key":"10.1016\/j.eswa.2026.131514_bib0022","series-title":"Machine learning and knowledge discovery in databases: European conference, ECML PKDD 2014, Nancy, France, September 15\u201319, 2014. proceedings, Part I 14","first-page":"466","article-title":"Importance weighted inductive transfer learning for regression","author":"Garcke","year":"2014"},{"key":"10.1016\/j.eswa.2026.131514_bib0023","first-page":"6453","article-title":"Federated principal component analysis","volume":"33","author":"Grammenos","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"4","key":"10.1016\/j.eswa.2026.131514_bib0024","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1007\/s10845-023-02126-z","article-title":"Federated transfer learning for auxiliary classifier generative adversarial networks: Framework and industrial application","volume":"35","author":"Guo","year":"2024","journal-title":"Journal of Intelligent Manufacturing"},{"key":"10.1016\/j.eswa.2026.131514_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127725","article-title":"Enhancing decomposition-based hybrid models for forecasting multivariate and multi-source time series by federated transfer learning","volume":"283","author":"He","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131514_bib0026","unstructured":"Himeur, Y., Varlamis, I., Kheddar, H., Amira, A., Atalla, S., Singh, Y., Bensaali, F., & Mansoor, W. (2023). Federated learning for computer vision. arXiv preprint arXiv: 2308.13558,."},{"key":"10.1016\/j.eswa.2026.131514_bib0027","doi-asserted-by":"crossref","first-page":"36895","DOI":"10.1109\/JIOT.2024.3433460","article-title":"Personalized federated transfer learning for cycle-life prediction of lithium-ion batteries in heterogeneous clients with data privacy protection","volume":"11","author":"Huang","year":"2024","journal-title":"IEEE Internet of Things Journal"},{"issue":"2","key":"10.1016\/j.eswa.2026.131514_bib0028","doi-asserted-by":"crossref","first-page":"40","DOI":"10.3390\/technologies11020040","article-title":"A review of deep transfer learning and recent advancements","volume":"11","author":"Iman","year":"2023","journal-title":"Technologies"},{"key":"10.1016\/j.eswa.2026.131514_bib0029","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13042-024-02119-1","article-title":"Emerging trends in federated learning: From model fusion to federated x learning","volume":"15","author":"Ji","year":"2024","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"10.1016\/j.eswa.2026.131514_bib0030","series-title":"2022\u202fIEEE 28th international conference on parallel and distributed systems (ICPADS)","first-page":"786","article-title":"FedDyn: A dynamic and efficient federated distillation approach on recommender system","author":"Jin","year":"2023"},{"key":"10.1016\/j.eswa.2026.131514_bib0031","series-title":"2020 20th IEEE\/ACM international symposium on cluster, cloud and internet computing (CCGRID)","first-page":"410","article-title":"Two-phase multi-party computation enabled privacy-preserving federated learning","author":"Kanagavelu","year":"2020"},{"key":"10.1016\/j.eswa.2026.131514_bib0032","series-title":"Proceedings of NAACL-HLT","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume":"vol. 1","author":"Kenton","year":"2019"},{"key":"10.1016\/j.eswa.2026.131514_bib0033","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H. B., Ramage, D., & Richt\u00e1rik, P. (2016). Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv: 1610.02527."},{"key":"10.1016\/j.eswa.2026.131514_bib0034","first-page":"84","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.131514_bib0035","series-title":"International conference on data analytics & management","first-page":"449","article-title":"Ftl-emo: Federated transfer learning for privacy preserved biomarker-based automatic emotion recognition","author":"Kumar","year":"2023"},{"key":"10.1016\/j.eswa.2026.131514_bib0036","series-title":"2020\u202fIEEE International conference on cloud computing in emerging markets (CCEM)","first-page":"52","article-title":"Federated k-means clustering: A novel edge ai based approach for privacy preservation","author":"Kumar","year":"2020"},{"key":"10.1016\/j.eswa.2026.131514_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2022.102139","article-title":"Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images","volume":"102","author":"Kumar","year":"2022","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"10.1016\/j.eswa.2026.131514_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108093","article-title":"CDKT-FL: Cross-device knowledge transfer using proxy dataset in federated learning","volume":"133","author":"Le","year":"2024","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"10","key":"10.1016\/j.eswa.2026.131514_bib0039","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.3390\/math11102320","article-title":"A mathematical investigation of hallucination and creativity in GPT models","volume":"11","author":"Lee","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.eswa.2026.131514_bib0040","series-title":"IEEE infocom 2023-IEEE conference on computer communications","first-page":"1","article-title":"FedSDG-FS: Efficient and secure feature selection for vertical federated learning","author":"Li","year":"2023"},{"key":"10.1016\/j.eswa.2026.131514_bib0041","series-title":"Federated and transfer learning","first-page":"357","article-title":"Federated transfer reinforcement learning for autonomous driving","author":"Liang","year":"2022"},{"key":"10.1016\/j.eswa.2026.131514_bib0042","series-title":"Computer vision \u2013 ECCV 2014","first-page":"740","article-title":"Microsoft COCO: Common objects in context","author":"Lin","year":"2014"},{"key":"10.1016\/j.eswa.2026.131514_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111587","article-title":"Data privacy protection: A novel federated transfer learning scheme for bearing fault diagnosis","volume":"291","author":"Liu","year":"2024","journal-title":"Knowledge-Based Systems"},{"issue":"4","key":"10.1016\/j.eswa.2026.131514_bib0044","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/MIS.2020.2988525","article-title":"A secure federated transfer learning framework","volume":"35","author":"Liu","year":"2020","journal-title":"IEEE Intelligent Systems"},{"key":"10.1016\/j.eswa.2026.131514_bib0045","doi-asserted-by":"crossref","first-page":"140","DOI":"10.56553\/popets-2023-0009","article-title":"Sok: Secure aggregation based on cryptographic schemes for federated learning","author":"Mansouri","year":"2023","journal-title":"Proceedings on Privacy Enhancing Technologies"},{"key":"10.1016\/j.eswa.2026.131514_bib0046","series-title":"Artificial intelligence and statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.eswa.2026.131514_bib0047","unstructured":"Mouzannar, H., Rizk, Y., & Awad, M. (2018). Multimodal Damage Identification for Humanitarian Computing. UCI Machine Learning Repository. 10.24432\/C52P6P."},{"issue":"2","key":"10.1016\/j.eswa.2026.131514_bib0048","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","article-title":"A decade survey of transfer learning (2010\u20132020)","volume":"1","author":"Niu","year":"2020","journal-title":"IEEE Transactions on Artificial Intelligence"},{"issue":"5","key":"10.1016\/j.eswa.2026.131514_bib0049","doi-asserted-by":"crossref","first-page":"263","DOI":"10.3390\/info13050263","article-title":"A review on federated learning and machine learning approaches: Categorization, application areas, and blockchain technology","volume":"13","author":"Ogundokun","year":"2022","journal-title":"Information"},{"key":"10.1016\/j.eswa.2026.131514_bib0050","first-page":"204","article-title":"Discriminability-based transfer between neural networks","volume":"5","author":"Pratt","year":"1992","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.131514_bib0051","series-title":"Proceedings of the ninth national conference on artificial intelligence-volume 2","first-page":"584","article-title":"Direct transfer of learned information among neural networks","author":"Pratt","year":"1991"},{"key":"10.1016\/j.eswa.2026.131514_bib0052","doi-asserted-by":"crossref","unstructured":"Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Wang, W., Banbury, C., Ye, C., Akin, B. et al. (2024). MobileNetv4-universal models for the mobile ecosystem. arXiv preprint arXiv: 2404.10518,.","DOI":"10.1007\/978-3-031-73661-2_5"},{"key":"10.1016\/j.eswa.2026.131514_bib0053","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, L., (2018). Improving language understanding by generative pre-training, San Francisco, CA, USA, https:\/\/api.semanticscholar.org\/CorpusID:49313245."},{"key":"10.1016\/j.eswa.2026.131514_bib0054","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107763","article-title":"Designing ECG monitoring healthcare system with federated transfer learning and explainable AI","volume":"236","author":"Raza","year":"2022","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.131514_bib0055","unstructured":"Reyes-Ortiz, J., Anguita, D., Ghio, A., Oneto, L., & Parra, X. (2012). Human activity recognition using smartphones. UCI Machine Learning Repository. 10.24432\/C54S4K."},{"issue":"1","key":"10.1016\/j.eswa.2026.131514_bib0056","doi-asserted-by":"crossref","first-page":"35","DOI":"10.3233\/IA-200075","article-title":"Federated transfer learning: Concept and applications","volume":"15","author":"Saha","year":"2021","journal-title":"Intelligenza Artificiale"},{"issue":"3","key":"10.1016\/j.eswa.2026.131514_bib0057","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1147\/rd.33.0210","article-title":"Some studies in machine learning using the game of checkers","volume":"3","author":"Samuel","year":"1959","journal-title":"IBM Journal of Research and Development"},{"key":"10.1016\/j.eswa.2026.131514_bib0058","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127253","article-title":"Prediction of ailments using federated transfer learning and weight penalty-rational tanh-RNN","volume":"276","author":"Shahnazeer","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131514_bib0059","series-title":"2019\u202fIEEE International conference on big data (big data)","first-page":"2569","article-title":"Secure and efficient federated transfer learning","author":"Sharma","year":"2019"},{"key":"10.1016\/j.eswa.2026.131514_bib0060","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556."},{"key":"10.1016\/j.eswa.2026.131514_bib0061","series-title":"17th international symposium on medical information processing and analysis","first-page":"351","article-title":"Secure neuroimaging analysis using federated learning with homomorphic encryption","volume":"vol. 12088","author":"Stripelis","year":"2021"},{"key":"10.1016\/j.eswa.2026.131514_bib0062","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015"},{"key":"10.1016\/j.eswa.2026.131514_bib0063","series-title":"Artificial neural networks and machine learning\u2013ICANN 2018: 27th international conference on artificial neural networks, Rhodes, Greece, October 4\u20137, 2018, proceedings, Part III 27","first-page":"270","article-title":"A survey on deep transfer learning","author":"Tan","year":"2018"},{"key":"10.1016\/j.eswa.2026.131514_bib0064","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.129639","article-title":"A privacy-preserving framework integrating federated learning and transfer learning for wind power forecasting","volume":"286","author":"Tang","year":"2024","journal-title":"Energy"},{"issue":"12","key":"10.1016\/j.eswa.2026.131514_bib0065","doi-asserted-by":"crossref","first-page":"4318","DOI":"10.3390\/s22124318","article-title":"Design space exploration of a sparse MobileNetv2 using high-level synthesis and sparse matrix techniques on FPGAs","volume":"22","author":"Tragoudaras","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.eswa.2026.131514_bib0066","series-title":"Ict4ageingwell","article-title":"The mobiact dataset: Recognition of activities of daily living using smartphones","author":"Vavoulas","year":"2016"},{"key":"10.1016\/j.eswa.2026.131514_bib0067","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1016\/j.procs.2024.03.289","article-title":"Identification of kidney disorders in decentralized healthcare systems through federated transfer learning","volume":"233","author":"Vekaria","year":"2024","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.eswa.2026.131514_bib0068","series-title":"Globecom 2022-2022 IEEE global communications conference","first-page":"3875","article-title":"Communication-efficient and privacy-preserving feature-based federated transfer learning","author":"Wang","year":"2022"},{"issue":"6","key":"10.1016\/j.eswa.2026.131514_bib0069","doi-asserted-by":"crossref","first-page":"4088","DOI":"10.1109\/TII.2021.3088057","article-title":"Federated transfer learning based cross-domain prediction for smart manufacturing","volume":"18","author":"Wang","year":"2022","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.eswa.2026.131514_bib0070","series-title":"2017\u202fIEEE Conference on computer vision and pattern recognition (CVPR)","first-page":"3462","article-title":"Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases","author":"Wang","year":"2017"},{"key":"10.1016\/j.eswa.2026.131514_bib0071","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"Journal of Big Data"},{"issue":"7","key":"10.1016\/j.eswa.2026.131514_bib0072","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1109\/TKDE.2017.2685597","article-title":"Online transfer learning with multiple homogeneous or heterogeneous sources","volume":"29","author":"Wu","year":"2017","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.eswa.2026.131514_bib0073","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2024.102499","article-title":"FTSDC: A novel federated transfer learning strategy for bearing cross-machine fault diagnosis based on dual-correction training","volume":"61","author":"Yan","year":"2024","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.eswa.2026.131514_bib0074","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113480","article-title":"Balance recovery and collaborative adaptation approach for federated fault diagnosis of inconsistent machine groups","volume":"317","author":"Yang","year":"2025","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.eswa.2026.131514_bib0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112605","article-title":"A dynamic barycenter bridging network for federated transfer fault diagnosis in machine groups","volume":"230","author":"Yang","year":"2025","journal-title":"Mechanical Systems and Signal Processing"},{"issue":"2","key":"10.1016\/j.eswa.2026.131514_bib0076","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/TNSE.2020.2996612","article-title":"Fedsteg: A federated transfer learning framework for secure image steganalysis","volume":"8","author":"Yang","year":"2021","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"10.1016\/j.eswa.2026.131514_bib0077","series-title":"2022\u202fFL Workshop of association for the advancement of artificial intelligenb26ce (AAAI)","article-title":"DiagNet: Machine fault diagnosis using federated transfer learning in low data regimes","author":"Yao","year":"2022"},{"key":"10.1016\/j.eswa.2026.131514_bib0078","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111633","article-title":"PfedKT: Personalized federated learning with dual knowledge transfer","volume":"292","author":"Yi","year":"2024","journal-title":"Knowledge-Based Systems"},{"issue":"6","key":"10.1016\/j.eswa.2026.131514_bib0079","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3460427","article-title":"A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions","volume":"54","author":"Yin","year":"2021","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"4","key":"10.1016\/j.eswa.2026.131514_bib0080","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1177\/14759217211029201","article-title":"Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions","volume":"21","author":"Zhang","year":"2022","journal-title":"Structural Health Monitoring"},{"issue":"1","key":"10.1016\/j.eswa.2026.131514_bib0081","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TMECH.2021.3065522","article-title":"Federated transfer learning for intelligent fault diagnostics using deep adversarial networks with data privacy","volume":"27","author":"Zhang","year":"2022","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"issue":"1","key":"10.1016\/j.eswa.2026.131514_bib0082","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jnlssr.2021.10.007","article-title":"Federated transfer learning for disaster classification in social computing networks","volume":"3","author":"Zhang","year":"2022","journal-title":"Journal of Safety Science and Resilience"},{"issue":"1","key":"10.1016\/j.eswa.2026.131514_bib0083","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jnlssr.2021.10.007","article-title":"Federated transfer learning for disaster classification in social computing networks","volume":"3","author":"Zhang","year":"2022","journal-title":"Journal of Safety Science and Resilience"},{"issue":"3","key":"10.1016\/j.eswa.2026.131514_bib0084","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1109\/TRPMS.2022.3194408","article-title":"Federated transfer learning for low-dose PET denoising: A pilot study with simulated heterogeneous data","volume":"7","author":"Zhou","year":"2022","journal-title":"IEEE Transactions on Radiation and Plasma Medical Sciences"},{"issue":"1","key":"10.1016\/j.eswa.2026.131514_bib0085","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2020","journal-title":"Proceedings of the IEEE"},{"issue":"1","key":"10.1016\/j.eswa.2026.131514_bib0086","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-93030-0","article-title":"Medical imaging deep learning with differential privacy","volume":"11","author":"Ziller","year":"2021","journal-title":"Scientific Reports"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426004276?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426004276?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T12:44:09Z","timestamp":1784897049000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426004276"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":86,"alternative-id":["S0957417426004276"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131514","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Breaking barriers in federated transfer learning: A systematic review on federated transfer learning for non-overlapping domains","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131514","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"131514"}}