{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:03:39Z","timestamp":1780509819658,"version":"3.54.1"},"reference-count":73,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T00:00:00Z","timestamp":1779753600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.neunet.2026.109190","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T15:24:41Z","timestamp":1780327481000},"page":"109190","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["BigOrthoATD.Net: A scalable and adaptable distributed deep learning framework for multi-class orthopedic classification across imaging modalities in low-resourced settings"],"prefix":"10.1016","volume":"203","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2527-0643","authenticated-orcid":false,"given":"Haider A.","family":"Alwzwazy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7296-5413","authenticated-orcid":false,"given":"Laith","family":"Alzubaidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9578-5706","authenticated-orcid":false,"given":"Zehui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ross","family":"Crawford","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7246-3974","authenticated-orcid":false,"given":"Omar","family":"Alnaseri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7517-0782","authenticated-orcid":false,"given":"Raja","family":"Jurdak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuantong","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2026.109190_bib0001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10278-025-01481-y","article-title":"Vision transformers in medical imaging: A comprehensive review of advancements and applications across multiple diseases","volume":"38","author":"Aburass","year":"2025","journal-title":"Journal of Imaging Informatics in Medicine"},{"issue":"1","key":"10.1016\/j.neunet.2026.109190_bib0002","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-06481-0","article-title":"Hybrid deep learning architecture for scalable and high-quality image compression","volume":"15","author":"Al-Khafaji","year":"2025","journal-title":"Scientific Reports"},{"key":"10.1016\/j.neunet.2026.109190_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.iswa.2024.200415","article-title":"Generalisable deep learning framework to overcome catastrophic forgetting","volume":"23","author":"Alammar","year":"2024","journal-title":"Intelligent Systems with Applications"},{"key":"10.1016\/j.neunet.2026.109190_bib0004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2025.01.034","article-title":"FracNet: An end-to-end deep learning framework for bone fracture detection","volume":"190","author":"Alwzwazy","year":"2025","journal-title":"Pattern Recognition Letters"},{"issue":"10","key":"10.1016\/j.neunet.2026.109190_bib0005","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10462-024-10878-0","article-title":"SSP: Self-supervised pertaining technique for classification of shoulder implants in x-ray medical images: A broad experimental study","volume":"57","author":"Alzubaidi","year":"2024","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.neunet.2026.109190_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.102953","article-title":"ATD learning: A secure, smart, and decentralised learning method for big data environments","volume":"118","author":"Alzubaidi","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109190_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2024.102779","article-title":"Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service","volume":"149","author":"Aminizadeh","year":"2024","journal-title":"Artificial Intelligence in Medicine"},{"issue":"5","key":"10.1016\/j.neunet.2026.109190_bib0008","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1016\/j.arth.2024.02.001","article-title":"Novel technique for the identification of hip implants using artificial intelligence","volume":"39","author":"Antonson","year":"2024","journal-title":"The Journal of Arthroplasty"},{"key":"10.1016\/j.neunet.2026.109190_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.103000","article-title":"Advances in medical image analysis with vision transformers: A comprehensive review","volume":"91","author":"Azad","year":"2024","journal-title":"Medical Image Analysis"},{"issue":"4","key":"10.1016\/j.neunet.2026.109190_bib0010","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1038\/s42256-024-00813-x","article-title":"Federated learning is not a cure-all for data ethics","volume":"6","author":"Bak","year":"2024","journal-title":"Nature Machine Intelligence"},{"issue":"1","key":"10.1016\/j.neunet.2026.109190_bib0011","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s13677-022-00377-4","article-title":"Federated learning in cloud-edge collaborative architecture: Key technologies, applications and challenges","volume":"11","author":"Bao","year":"2022","journal-title":"Journal of Cloud Computing"},{"issue":"1","key":"10.1016\/j.neunet.2026.109190_bib0012","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3429252","article-title":"Decentralised learning in federated deployment environments: A system-level survey","volume":"54","author":"Bellavista","year":"2021","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"10.1016\/j.neunet.2026.109190_bib0013","series-title":"2021 6th international conference on computer science and engineering (UBMK)","first-page":"429","article-title":"A communication efficient federated learning approach to multi chest diseases classification","author":"Cetinkaya","year":"2021"},{"key":"10.1016\/j.neunet.2026.109190_bib0014","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1251","article-title":"Xception: Deep learning with depthwise separable convolutions","author":"Chollet","year":"2017"},{"key":"10.1016\/j.neunet.2026.109190_bib0015","series-title":"Healthcare","first-page":"701","article-title":"Bridging the gap: From AI success in clinical trials to real-world healthcare implementation\u2013a narrative review","volume":"vol. 13","author":"El Arab","year":"2025"},{"issue":"11","key":"10.1016\/j.neunet.2026.109190_bib0016","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0260471","article-title":"Artificial intelligence in orthopaedics: A scoping review","volume":"16","author":"Federer","year":"2021","journal-title":"PLoS One"},{"key":"10.1016\/j.neunet.2026.109190_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107330","article-title":"Federated learning for COVID-19 screening from chest x-ray images","volume":"106","author":"Feki","year":"2021","journal-title":"Applied Soft Computing"},{"issue":"11","key":"10.1016\/j.neunet.2026.109190_bib0018","doi-asserted-by":"crossref","first-page":"413","DOI":"10.3390\/fi16110413","article-title":"A joint survey in decentralized federated learning and tinyML: A brief introduction to swarm learning","volume":"16","author":"Fragkou","year":"2024","journal-title":"Future Internet"},{"issue":"7","key":"10.1016\/j.neunet.2026.109190_bib0019","doi-asserted-by":"crossref","first-page":"2118","DOI":"10.1109\/TMI.2022.3220750","article-title":"A new framework of swarm learning consolidating knowledge from multi-center non-IID data for medical image segmentation","volume":"42","author":"Gao","year":"2022","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.neunet.2026.109190_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2024.105782","article-title":"Knowledge domain and frontier trends of artificial intelligence applied in solid organ transplantation: A visualization analysis","volume":"195","author":"Gong","year":"2025","journal-title":"International Journal of Medical Informatics"},{"key":"10.1016\/j.neunet.2026.109190_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.jnca.2023.103714","article-title":"A systematic review of federated learning: challenges, aggregation methods, and development tools","volume":"220","author":"Guendouzi","year":"2023","journal-title":"Journal of Network and Computer Applications"},{"key":"10.1016\/j.neunet.2026.109190_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.102963","article-title":"A survey of large language models for healthcare: From data, technology, and applications to accountability and ethics","volume":"118","author":"He","year":"2025","journal-title":"Information Fusion"},{"issue":"10","key":"10.1016\/j.neunet.2026.109190_bib0023","doi-asserted-by":"crossref","first-page":"3728","DOI":"10.3390\/s22103728","article-title":"FedSGDCOVID: Federated SGD COVID-19 detection under local differential privacy using chest x-ray images and symptom information","volume":"22","author":"Ho","year":"2022","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.neunet.2026.109190_bib0024","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1007\/s42235-023-00477-0","article-title":"Transfer learning-based class imbalance-aware shoulder implant classification from x-ray images","volume":"21","author":"Jindal","year":"2024","journal-title":"Journal of Bionic Engineering"},{"key":"10.1016\/j.neunet.2026.109190_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110583","article-title":"Class imbalance-aware domain specific transfer learning approach for medical image classification: Application on COVID-19 detection","volume":"150","author":"Jindal","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.neunet.2026.109190_bib0026","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.neunet.2023.04.007","article-title":"Exploring personalization via federated representation learning on non-IID data","volume":"163","author":"Jing","year":"2023","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109190_bib0027","series-title":"2024 47th international conference on telecommunications and signal processing (TSP)","first-page":"92","article-title":"An ensemble voting approach for shoulder implant classification from x-ray images","author":"Kablan","year":"2024"},{"issue":"3","key":"10.1016\/j.neunet.2026.109190_bib0028","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.arth.2020.10.021","article-title":"Artificial intelligence to identify arthroplasty implants from radiographs of the knee","volume":"36","author":"Karnuta","year":"2021","journal-title":"The Journal of Arthroplasty"},{"key":"10.1016\/j.neunet.2026.109190_bib0029","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"14","key":"10.1016\/j.neunet.2026.109190_bib0030","doi-asserted-by":"crossref","first-page":"16301","DOI":"10.1109\/JSEN.2021.3076767","article-title":"Blockchain-federated-learning and deep learning models for covid-19 detection using CT imaging","volume":"21","author":"Kumar","year":"2021","journal-title":"IEEE Sensors Journal"},{"issue":"10","key":"10.1016\/j.neunet.2026.109190_bib0031","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1016\/j.jse.2023.03.028","article-title":"Artificial intelligence for automated identification of total shoulder arthroplasty implants","volume":"32","author":"Kunze","year":"2023","journal-title":"Journal of Shoulder and Elbow Surgery"},{"key":"10.1016\/j.neunet.2026.109190_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123655","article-title":"MIT-FRNet: Modality-invariant temporal representation learning-based feature reconstruction network for missing modalities","volume":"249","author":"Li","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.neunet.2026.109190_bib0033","article-title":"DNUNet: A lightweight adaptive medical image segmentation network based on dual-path multilevel interactive convolution and norm sparse fusion module","volume":"195","author":"Li","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109190_bib0034","article-title":"Deep learning for automated classification of hip hardware on radiographs","volume":"10","author":"Ma","year":"2024","journal-title":"Journal of Imaging Informatics in Medicine"},{"key":"10.1016\/j.neunet.2026.109190_bib0035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10278-024-01263-y","article-title":"Deep learning for automated classification of hip hardware on radiographs","volume":"38","author":"Ma","year":"2024","journal-title":"Journal of Imaging Informatics in Medicine"},{"key":"10.1016\/j.neunet.2026.109190_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2025.105802","article-title":"Hip prosthesis failure prediction through radiological deep sequence learning","volume":"196","author":"Masciulli","year":"2025","journal-title":"International Journal of Medical Informatics"},{"key":"10.1016\/j.neunet.2026.109190_bib0037","series-title":"Artificial intelligence and statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"issue":"12","key":"10.1016\/j.neunet.2026.109190_bib0038","doi-asserted-by":"crossref","first-page":"7895","DOI":"10.1007\/s00330-024-10834-0","article-title":"Class imbalance on medical image classification: Towards better evaluation practices for discrimination and calibration performance","volume":"34","author":"Mosquera","year":"2024","journal-title":"European Radiology"},{"issue":"6","key":"10.1016\/j.neunet.2026.109190_bib0039","doi-asserted-by":"crossref","first-page":"3909","DOI":"10.1007\/s10278-025-01484-9","article-title":"Federated learning framework for brain tumor detection using MRI images in non-IID data distributions","volume":"38","author":"Muntaqim","year":"2025","journal-title":"Journal of Imaging Informatics in Medicine"},{"issue":"1","key":"10.1016\/j.neunet.2026.109190_bib0040","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-95390-3","article-title":"Addressing cross-population domain shift in chest X-ray classification through supervised adversarial domain adaptation","volume":"15","author":"Musa","year":"2025","journal-title":"Scientific Reports"},{"issue":"8","key":"10.1016\/j.neunet.2026.109190_bib0041","doi-asserted-by":"crossref","DOI":"10.1002\/mp.18064","article-title":"A novel federated learning framework for medical imaging: Resource-efficient approach combining PCA with early stopping","volume":"52","author":"Nanekaran","year":"2025","journal-title":"Medical Physics"},{"key":"10.1016\/j.neunet.2026.109190_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.health.2023.100135","article-title":"Blockchain for medical collaboration: A federated learning-based approach for multi-class respiratory disease classification","volume":"3","author":"Noman","year":"2023","journal-title":"Healthcare Analytics"},{"key":"10.1016\/j.neunet.2026.109190_bib0043","unstructured":"Oh, J., Kim, S., & Yun, S.-Y. (2021). FedBABU: Towards enhanced representation for federated image classification. arXiv preprint arXiv: 2106.06042."},{"issue":"4","key":"10.1016\/j.neunet.2026.109190_bib0044","article-title":"Automated identification of orthopedic implants on radiographs using deep learning","volume":"3","author":"Patel","year":"2021","journal-title":"Radiology: Artificial Intelligence"},{"issue":"5","key":"10.1016\/j.neunet.2026.109190_bib0045","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.3390\/s23052381","article-title":"Effective techniques for multimodal data fusion: A comparative analysis","volume":"23","author":"Paw\u0142owski","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.neunet.2026.109190_bib0046","doi-asserted-by":"crossref","first-page":"5731","DOI":"10.1109\/ACCESS.2023.3348817","article-title":"KONet: Toward a weighted ensemble learning model for knee osteoporosis classification","volume":"12","author":"Rasool","year":"2024","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.neunet.2026.109190_bib0047","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1016\/j.arth.2023.09.025","article-title":"THA-AID: Deep learning tool for total hip arthroplasty automatic implant detection with uncertainty and outlier quantification","volume":"39","author":"Rouzrokh","year":"2024","journal-title":"The Journal of Arthroplasty"},{"issue":"6","key":"10.1016\/j.neunet.2026.109190_bib0048","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1038\/s41591-022-01768-5","article-title":"Swarm learning for decentralized artificial intelligence in cancer histopathology","volume":"28","author":"Saldanha","year":"2022","journal-title":"Nature Medicine"},{"issue":"1","key":"10.1016\/j.neunet.2026.109190_bib0049","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/s43856-024-00722-5","article-title":"Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging","volume":"5","author":"Saldanha","year":"2025","journal-title":"Communications Medicine"},{"issue":"10","key":"10.1016\/j.neunet.2026.109190_bib0050","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s10462-024-10884-2","article-title":"Handling imbalanced medical datasets: Review of a decade of research","volume":"57","author":"Salmi","year":"2024","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.neunet.2026.109190_bib0051","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1007\/s43465-021-00529-9","article-title":"Knee implant identification by fine-tuning deep learning models","volume":"55","author":"Sharma","year":"2021","journal-title":"Indian Journal of Orthopaedics"},{"key":"10.1016\/j.neunet.2026.109190_bib0052","series-title":"Healthcare","first-page":"580","article-title":"A novel hybrid machine learning based system to classify shoulder implant manufacturers","volume":"vol. 10","author":"Sivari","year":"2022"},{"key":"10.1016\/j.neunet.2026.109190_bib0053","series-title":"International conference on computational science","first-page":"433","article-title":"CXR-FL: Deep learning-based chest X-ray image analysis using federated learning","author":"\u015alazyk","year":"2022"},{"issue":"2","key":"10.1016\/j.neunet.2026.109190_bib0054","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/JBHI.2020.3032060","article-title":"Measuring domain shift for deep learning in histopathology","volume":"25","author":"Stacke","year":"2020","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"6","key":"10.1016\/j.neunet.2026.109190_bib0055","doi-asserted-by":"crossref","first-page":"482","DOI":"10.3390\/jpm11060482","article-title":"Artificial intelligence-based recognition of different types of shoulder implants in X-ray scans based on dense residual ensemble-network for personalized medicine","volume":"11","author":"Sultan","year":"2021","journal-title":"Journal of Personalized Medicine"},{"issue":"1","key":"10.1016\/j.neunet.2026.109190_bib0056","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/s10916-024-02105-8","article-title":"Comparison of vision transformers and convolutional neural networks in medical image analysis: A systematic review","volume":"48","author":"Takahashi","year":"2024","journal-title":"Journal of Medical Systems"},{"key":"10.1016\/j.neunet.2026.109190_bib0057","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcot.2023.102312","article-title":"Evaluation of machine learning models to identify hip arthroplasty implants using transfer learning algorithms","volume":"47","author":"Tiwari","year":"2023","journal-title":"Journal of Clinical Orthopaedics and Trauma"},{"key":"10.1016\/j.neunet.2026.109190_bib0058","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.jor.2022.05.013","article-title":"Application of deep learning algorithm in automated identification of knee arthroplasty implants from plain radiographs using transfer learning models: Are algorithms better than humans?","volume":"32","author":"Tiwari","year":"2022","journal-title":"Journal of Orthopaedics"},{"key":"10.1016\/j.neunet.2026.109190_bib0059","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1016\/j.csbj.2020.04.005","article-title":"Classifying shoulder implants in X-ray images using deep learning","volume":"18","author":"Urban","year":"2020","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"10.1016\/j.neunet.2026.109190_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103084","article-title":"Privacy-preserving heterogeneous multi-modal sensor data fusion via federated learning for smart healthcare","volume":"120","author":"Wang","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.109190_bib0061","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107874","article-title":"Linkage on security, privacy and fairness in federated learning: New balances and new perspectives","volume":"192","author":"Wang","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109190_bib0062","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107573","article-title":"EScarcityS: A framework for enhancing medical image classification performance in scarcity of trainable samples scenarios","volume":"189","author":"Wang","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109190_bib0063","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103569","article-title":"Lightweight multi-stage aggregation transformer for robust medical image segmentation","volume":"103","author":"Wang","year":"2025","journal-title":"Medical Image Analysis"},{"issue":"7862","key":"10.1016\/j.neunet.2026.109190_bib0064","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1038\/s41586-021-03583-3","article-title":"Swarm learning for decentralized and confidential clinical machine learning","volume":"594","author":"Warnat-Herresthal","year":"2021","journal-title":"Nature"},{"key":"10.1016\/j.neunet.2026.109190_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.108220","article-title":"Privacy-preserving personalized federated prompt learning for vision-language models","volume":"195","author":"Wu","year":"2026","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109190_bib0066","article-title":"Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: A multi-center diagnostic study","volume":"12","author":"Xie","year":"2025","journal-title":"Frontiers in Medicine"},{"issue":"3","key":"10.1016\/j.neunet.2026.109190_bib0067","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3625558","article-title":"Heterogeneous federated learning: State-of-the-art and research challenges","volume":"56","author":"Ye","year":"2023","journal-title":"ACM Computing Surveys"},{"issue":"5","key":"10.1016\/j.neunet.2026.109190_bib0068","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.1007\/s11280-019-00764-z","article-title":"Deep learning for heterogeneous medical data analysis","volume":"23","author":"Yue","year":"2020","journal-title":"World Wide Web"},{"key":"10.1016\/j.neunet.2026.109190_bib0069","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107278","article-title":"StoCFL: A stochastically clustered federated learning framework for non-IID data with dynamic client participation","volume":"187","author":"Zeng","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.neunet.2026.109190_bib0070","series-title":"International conference on medical image computing and computer-assisted intervention","first-page":"707","article-title":"Tackling data heterogeneity in federated learning via loss decomposition","author":"Zeng","year":"2024"},{"key":"10.1016\/j.neunet.2026.109190_bib0071","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103728","article-title":"Rethinking data imbalance in class incremental surgical instrument segmentation","volume":"105","author":"Zhao","year":"2025","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.neunet.2026.109190_bib0072","doi-asserted-by":"crossref","DOI":"10.1007\/s10462-026-11540-7","article-title":"A systematic review of artificial intelligence-driven decentralized learning in healthcare: Emerging techniques, applications, challenges and future directions","volume":"59","author":"Zhao","year":"2026","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.neunet.2026.109190_bib0073","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"2921","article-title":"Learning deep features for discriminative localization","author":"Zhou","year":"2016"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026006519?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026006519?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T17:18:14Z","timestamp":1780507094000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026006519"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":73,"alternative-id":["S0893608026006519"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109190","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"BigOrthoATD.Net: A scalable and adaptable distributed deep learning framework for multi-class orthopedic classification across imaging modalities in low-resourced settings","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109190","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"109190"}}