{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T15:21:21Z","timestamp":1769008881800,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"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":["Cogn Comput"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s12559-025-10531-0","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T08:55:30Z","timestamp":1765443330000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-Preserving Skin Disease Detection Using One-Shot Federated Learning Approach"],"prefix":"10.1007","volume":"17","author":[{"given":"Tasnim\u00a0Ur","family":"Rahaman\u00a0Anas","sequence":"first","affiliation":[]},{"given":"Qaiser","family":"Razi","sequence":"additional","affiliation":[]},{"given":"Sparsh","family":"Bajoria","sequence":"additional","affiliation":[]},{"given":"Vikas","family":"Hassija","sequence":"additional","affiliation":[]},{"given":"GSS","family":"Chalapathi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"10531_CR1","doi-asserted-by":"crossref","unstructured":"Leiter U, Garbe C. Epidemiology of melanoma and nonmelanoma skin cancer\u2014the role of sunlight. Sunlight, vitamin D and skin cancer; 2008. pp. 89\u2013103.","DOI":"10.1007\/978-0-387-77574-6_8"},{"key":"10531_CR2","unstructured":"McMahan B, Moore E, Ramage D, Hampson S, y\u00a0Arcas BA. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR; 2017. pp. 1273\u20131282."},{"issue":"1","key":"10531_CR3","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1109\/TCBB.2022.3155774","volume":"20","author":"H Wang","year":"2022","unstructured":"Wang H, Wu X. Ipp: An intelligent privacy-preserving scheme for detecting interactions in genome association studies. IEEE\/ACM Trans Comput Biol Bioinf. 2022;20(1):455\u201364.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf."},{"key":"10531_CR4","unstructured":"Guha N, Talwalkar A, Smith V. One-shot federated learning. 2019. arXiv preprint arXiv:1902.11175"},{"key":"10531_CR5","unstructured":"Wang T, Zhu JY, Torralba A, Efros AA. Dataset distillation. 2018. arXiv preprint arXiv:1811.10959"},{"key":"10531_CR6","unstructured":"PIB. (PIB). Update on ratio of patients and doctors nurses. Accessed 12 December 2023. [Online]. Available: https:\/\/pib.gov.in\/PressReleasePage.aspx?PRID=1985423"},{"key":"10531_CR7","unstructured":"T.\u00a0of\u00a0India. Presence of \u2018quacks\u2019 worries skin doctors. Accessed 10 October 2023. [Online]. Available: https:\/\/timesofindia.indiatimes.com\/city\/nagpur\/presence-of-quacks-worries-skin-doctors\/articleshow\/104299159.cms"},{"key":"10531_CR8","first-page":"144","volume":"2017","author":"MN Islam","year":"2017","unstructured":"Islam MN, Gallardo-Alvarado J, Abu M, Salman NA, Rengan SP, Said S, et al. IEEE 8th control and system graduate research colloquium (ICSGRC). IEEE. 2017;2017:144\u20138.","journal-title":"IEEE."},{"issue":"5","key":"10531_CR9","doi-asserted-by":"publisher","first-page":"396","DOI":"10.22161\/ijaers.6.5.53","volume":"6","author":"V Pugazhenthi","year":"2019","unstructured":"Pugazhenthi V, Naik SK, Joshi AD, Manerkar SS, Nagvekar VU, Naik KP, et al. Skin disease detection and classification. Int J Adv Eng Res Sci (IJAERS). 2019;6(5):396\u2013400.","journal-title":"Int J Adv Eng Res Sci (IJAERS)."},{"issue":"1","key":"10531_CR10","first-page":"80","volume":"2","author":"K Polat","year":"2020","unstructured":"Polat K, Koc KO. Detection of skin diseases from dermoscopy image using the combination of convolutional neural network and one-versus-all. J Artif Intell Syst. 2020;2(1):80\u201397.","journal-title":"J Artif Intell Syst."},{"key":"10531_CR11","unstructured":"Goetz J, Tewari A. Federated learning via synthetic data. 2020. arXiv preprint arXiv:2008.04489"},{"key":"10531_CR12","doi-asserted-by":"crossref","unstructured":"Roth HR, Chang K, Singh P, Neumark N, Li W, Gupta V, Gupta S, Qu L, Ihsani A, Bizzo BC et\u00a0al. Federated learning for breast density classification: A real-world implementation. In MICCAI workshop on domain adaptation and representation transfer. Springer; 2020. pp. 181\u2013191.","DOI":"10.1007\/978-3-030-60548-3_18"},{"key":"10531_CR13","doi-asserted-by":"crossref","unstructured":"Song R, Liu D, Chen DZ, Festag A, Trinitis C, Schulz M, et al. international joint conference on neural networks (IJCNN). IEEE. 2023;2023:1\u201310.","DOI":"10.1109\/IJCNN54540.2023.10191879"},{"key":"10531_CR14","first-page":"21414","volume":"35","author":"J Zhang","year":"2022","unstructured":"Zhang J, Chen C, Li B, Lyu L, Wu S, Ding S, et al. Dense: Data-free one-shot federated learning. Adv Neural Inf Process Sys. 2022;35:21414\u201328.","journal-title":"Adv Neural Inf Process Sys."},{"key":"10531_CR15","doi-asserted-by":"publisher","first-page":"83562","DOI":"10.1109\/ACCESS.2023.3301162","volume":"11","author":"M Abaoud","year":"2023","unstructured":"Abaoud M, Almuqrin MA, Khan MF. Advancing federated learning through novel mechanism for privacy preservation in healthcare applications. IEEE Access. 2023;11:83562\u201379.","journal-title":"IEEE Access."},{"issue":"2","key":"10531_CR16","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1109\/JBHI.2022.3149288","volume":"27","author":"MN Hossen","year":"2022","unstructured":"Hossen MN, Panneerselvam V, Koundal D, Ahmed K, Bui FM, Ibrahim SM. Federated machine learning for detection of skin diseases and enhancement of internet of medical things (iomt) security. IEEE J Biomed Health Inform. 2022;27(2):835\u201341.","journal-title":"IEEE J Biomed Health Inform."},{"key":"10531_CR17","doi-asserted-by":"crossref","unstructured":"Li Z, Wu H, Lu Y, Ai B, Zhong Z, Zhang Y. Matching game for multi-task federated learning in internet of vehicles. IEEE Trans Veh Technol. 2023.","DOI":"10.1109\/TVT.2023.3315050"},{"key":"10531_CR18","unstructured":"Jhunjhunwala D, Wang S, Joshi G. Fedfisher: Leveraging fisher information for one-shot federated learning. In International conference on artificial intelligence and statistics. PMLR; 2024. pp. 1612\u20131620."},{"key":"10531_CR19","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25."},{"issue":"4","key":"10531_CR20","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/LNET.2022.3200724","volume":"4","author":"R Gupta","year":"2022","unstructured":"Gupta R, Saxena D, Gupta I, Makkar A, Singh AK. Quantum machine learning driven malicious user prediction for cloud network communications. IEEE Netw Lett. 2022;4(4):174\u20138.","journal-title":"IEEE Netw Lett."},{"key":"10531_CR21","doi-asserted-by":"crossref","unstructured":"Chatterjee P, Das D, Rawat DB. Federated learning empowered recommendation model for financial consumer services. IEEE Trans Consum Electron. 2023.","DOI":"10.1109\/TCE.2023.3339702"},{"issue":"3","key":"10531_CR22","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/s00354-022-00185-z","volume":"40","author":"R Gupta","year":"2022","unstructured":"Gupta R, Singh AK. A differential approach for data and classification service-based privacy-preserving machine learning model in cloud environment. N Gener Comput. 2022;40(3):737\u201364.","journal-title":"N Gener Comput."},{"issue":"4","key":"10531_CR23","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1109\/LNET.2022.3215248","volume":"4","author":"R Gupta","year":"2022","unstructured":"Gupta R, Saxena D, Gupta I, Singh AK. Differential and triphase adaptive learning-based privacy-preserving model for medical data in cloud environment. IEEE Netw Lett. 2022;4(4):217\u201321.","journal-title":"IEEE Netw Lett."},{"key":"10531_CR24","unstructured":"Yang T, Andrew G, Eichner H, Sun H, Li W, Kong N, Ramage D, Beaufays F. Applied federated learning: Improving google keyboard query suggestions. 2018. arXiv preprint arXiv:1812.02903."},{"issue":"3","key":"10531_CR25","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1007\/s12559-023-10243-3","volume":"16","author":"J Qin","year":"2024","unstructured":"Qin J, Yu H, Liang W, Ding D. Video summarization using knowledge distillation-based attentive network. Cogn Comput. 2024;16(3):1022\u201331.","journal-title":"Cogn Comput."},{"issue":"1","key":"10531_CR26","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TPAMI.2023.3322540","volume":"46","author":"S Lei","year":"2023","unstructured":"Lei S, Tao D. A comprehensive survey of dataset distillation. IEEE Trans Pattern Anal Mach Intell. 2023;46(1):17\u201332.","journal-title":"IEEE Trans Pattern Anal Mach Intell."},{"key":"10531_CR27","doi-asserted-by":"crossref","unstructured":"Zhu D, Lei B, Zhang J, Fang Y, Xie Y, Zhang R, Xu D. Rethinking data distillation: Do not overlook calibration. In Proceedings of the IEEE\/CVF international conference on computer vision; 2023. pp. 4935\u20134945.","DOI":"10.1109\/ICCV51070.2023.00455"},{"issue":"1","key":"10531_CR28","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/TFUZZ.2024.3369944","volume":"33","author":"X Wu","year":"2024","unstructured":"Wu X, Zhang Y-T, Lai K-W, Yang M-Z, Yang G-L, Wang H-H. A novel centralized federated deep fuzzy neural network with multi-objectives neural architecture search for epistatic detection. IEEE Trans Fuzzy Syst. 2024;33(1):94\u2013107.","journal-title":"IEEE Trans Fuzzy Syst."},{"issue":"1","key":"10531_CR29","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1109\/TMC.2021.3070013","volume":"22","author":"S Itahara","year":"2021","unstructured":"Itahara S, Nishio T, Koda Y, Morikura M, Yamamoto K. Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data. IEEE Trans Mob Comput. 2021;22(1):191\u2013205.","journal-title":"IEEE Trans Mob Comput."},{"issue":"4","key":"10531_CR30","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1109\/TCBB.2023.3243932","volume":"21","author":"Y Tian","year":"2023","unstructured":"Tian Y, Wang S, Xiong J, Bi R, Zhou Z, Bhuiyan MZA. Robust and privacy-preserving decentralized deep federated learning training: Focusing on digital healthcare applications. IEEE\/ACM Trans Comput Biol Bioinf. 2023;21(4):890\u2013901.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf."},{"issue":"23","key":"10531_CR31","doi-asserted-by":"publisher","first-page":"33127","DOI":"10.1007\/s11042-021-11751-w","volume":"81","author":"AK Singh","year":"2022","unstructured":"Singh AK, Gupta R. A privacy-preserving model based on differential approach for sensitive data in cloud environment. Multimed Tools Appl. 2022;81(23):33127\u201350.","journal-title":"Multimed Tools Appl."},{"issue":"2","key":"10531_CR32","doi-asserted-by":"publisher","first-page":"2445","DOI":"10.1109\/JSYST.2022.3218894","volume":"17","author":"R Gupta","year":"2022","unstructured":"Gupta R, Gupta I, Singh AK, Saxena D, Lee C-N. An iot-centric data protection method for preserving security and privacy in cloud. IEEE Syst J. 2022;17(2):2445\u201354.","journal-title":"IEEE Syst J."},{"key":"10531_CR33","unstructured":"Dore A. (2023) Acne04 Dataset. Accessed 1 December 2023. [Online]. Available: https:\/\/universe.roboflow.com\/andrei-dore-5lz05\/acne04"},{"issue":"1","key":"10531_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl P, Rosendahl C, Kittler H. The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data. 2018;5(1):1\u20139.","journal-title":"Scientific data."},{"key":"10531_CR35","unstructured":"Hossain I. Skin Disease Image Dataset. Accessed 5 December 2023. [Online]. Available: https:\/\/www.kaggle.com\/datasets\/ismailpromus\/skin-diseases-image-dataset"},{"key":"10531_CR36","unstructured":"Nguyen T, Chen Z, Lee J. Dataset meta-learning from kernel ridge-regression. 2020. arXiv preprint arXiv:2011.00050."},{"key":"10531_CR37","unstructured":"Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning. PMLR; 2019. pp. 6105\u20136114."}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-025-10531-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-025-10531-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-025-10531-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T11:30:29Z","timestamp":1768908629000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-025-10531-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["10531"],"URL":"https:\/\/doi.org\/10.1007\/s12559-025-10531-0","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"1 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article contains no studies with human participants or animals performed by any authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"175"}}