{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T09:39:29Z","timestamp":1793353169504,"version":"4.5.0"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100024003","name":"Damanhour University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100024003","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Collaborative learning in healthcare faces challenges, including strict regulations and fragmented data. This research introduces a federated learning framework that employs swarm intelligence to augment communication and enhance the analysis of medical images. The method optimizes hyperparameters, selects features, and assigns aggregation weights to federated clients simultaneously by combining Particle Swarm Optimization (PSO) and the Firefly Algorithm (FA) with deep Convolutional Neural Networks (CNNs). The framework was tested on three medical datasets: COVID-19 chest X-rays (5,856 images), monkeypox skin images (569 images), and breast cancer mammograms (320 images). These datasets were shared among four fake healthcare institutions. It strives to strike a balance between privacy, communication costs, and classification accuracy. The results showed that the test was 96.71% accurate in detecting COVID-19, 96.06% accurate in classifying monkeypox, and 97.0% accurate in diagnosing breast cancer. The framework was able to handle noise and attacks from individuals who sought to disrupt it, which reduced communication rounds by 25\u201330%. A privacy-utility analysis revealed that there were acceptable trade-offs, with accuracy remaining above 94%. This study employs robust privacy measures and statistical validation. It also shows how to use medical AI in smaller healthcare settings without putting patients\u2019 privacy at risk.<\/jats:p>","DOI":"10.1038\/s41598-026-50882-8","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T19:13:54Z","timestamp":1778526834000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Federated learning with swarm intelligence for efficient and secure medical image analysis"],"prefix":"10.1038","volume":"16","author":[{"given":"M. A.","family":"SayedElahl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. M.","family":"Farouk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abd Elmounem","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elham","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"50882_CR1","doi-asserted-by":"crossref","unstructured":"Kutikuppala, L. V. S., V, V. G. R. S., Jonna, S., Suvvari, T. K. & Veeramreddy, N. R. Health-care databases and its role in transformation of medicine 47, 141\u2013143 (2020).","DOI":"10.4103\/jss.JSS_67_20"},{"key":"50882_CR2","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S. & Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR, USA (2017)."},{"key":"50882_CR3","unstructured":"McMahan, H. B., Yu, F., Richtarik, P., Suresh, A. & Bacon, D. Federated learning: Collaborative machine learning without centralized training data. In: Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS), Barcelona, Spain, pp. 5\u201310 (2016)."},{"issue":"4","key":"50882_CR4","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1504\/IJISTA.2023.134982","volume":"21","author":"MA SayedElahl","year":"2023","unstructured":"SayedElahl, M. A. A novel edge detection filter based on fractional order legendre-laguerre functions. Int. J. Intell. Syst. Technol. Appl. 21(4), 321\u2013343 (2023).","journal-title":"International Journal of Intelligent Systems Technologies and Applications"},{"issue":"3","key":"50882_CR5","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1504\/IJCAET.2022.125711","volume":"17","author":"MA SayedElahl","year":"2022","unstructured":"SayedElahl, M. A. & Farouk, R. M. Robust segmentation model for unshaped microarray spots using fractal transformation. Int. J. Comput. Aided Eng. Technol. 17(3), 271\u2013287 (2022).","journal-title":"International Journal of Computer Aided Engineering and Technology"},{"key":"50882_CR6","doi-asserted-by":"publisher","first-page":"3757","DOI":"10.1007\/s12652-019-01570-1","volume":"11","author":"F Moslehi","year":"2020","unstructured":"Moslehi, F. & Haeri, A. An evolutionary computation-based approach for feature selection. J. Ambient Intell. Humanized Comput. 11, 3757\u20133769 (2020).","journal-title":"Journal of Ambient Intelligence Humanized Computing"},{"key":"50882_CR7","volume-title":"New Ideas in Optimization","author":"D Corne","year":"1999","unstructured":"Corne, D. et al. New Ideas in Optimization (McGraw-Hill Ltd., UK, 1999)."},{"key":"50882_CR8","unstructured":"Kennedy, J., Eberhart, R., Corne, D., Dorigo, M. & Glover, F. The particle swarm optimization: Social adaptation in information processing. In: New Ideas in Optimization, pp. 379\u2013387 (1999)."},{"key":"50882_CR9","doi-asserted-by":"crossref","unstructured":"Fister, I. & \u017dumer, J. B. Memetic artificial bee colony algorithm for large-scale global optimization. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1\u20138. IEEE, Brisbane, Australia (2012).","DOI":"10.1109\/CEC.2012.6252938"},{"key":"50882_CR10","volume-title":"Nature-inspired Metaheuristic Algorithms","author":"X-S Yang","year":"2010","unstructured":"Yang, X.-S. Nature-inspired Metaheuristic Algorithms 2nd edn. (Luniver press, Frome, UK, 2010).","edition":"2"},{"key":"50882_CR11","doi-asserted-by":"publisher","unstructured":"Yang, X.-S. & Deb, S. Cuckoo search via l\u00e9vy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210\u2013214. IEEE, Coimbatore, India. https:\/\/doi.org\/10.1109\/NABIC.2009.5393690 (2009).","DOI":"10.1109\/NABIC.2009.5393690"},{"key":"50882_CR12","doi-asserted-by":"publisher","unstructured":"Yang, X.-S. A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65\u201374. Springer, Berlin, Germany. https:\/\/doi.org\/10.1007\/978-3-642-12538-6_6 (2010)","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"50882_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110122","volume":"260","author":"Y Hu","year":"2023","unstructured":"Hu, Y. et al. A federated feature selection algorithm based on particle swarm optimization under privacy protection. Knowl. Based Syst. 260, 110122 (2023).","journal-title":"Knowledge-Based Systems"},{"key":"50882_CR14","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.swevo.2013.06.001","volume":"13","author":"I Fister","year":"2013","unstructured":"Fister, I., Fister, I. Jr., Yang, X.-S. & Brest, J. A comprehensive review of firefly algorithms. Swarm Evolut. Comput. 13, 34\u201346 (2013).","journal-title":"Swarm evolutionary computation"},{"key":"50882_CR15","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1186\/s13677-024-00654-4","volume":"13","author":"AS Ghadi","year":"2024","unstructured":"Ghadi, A. S. et al. Enhancing patient healthcare with mobile edge computing and 5g: challenges and solutions for secure online health tools. J. Cloud Comput. 13, 654. https:\/\/doi.org\/10.1186\/s13677-024-00654-4 (2024).","journal-title":"Journal of Cloud Computing"},{"issue":"7","key":"50882_CR16","doi-asserted-by":"publisher","first-page":"8234","DOI":"10.1109\/TMC.2024.3423567","volume":"23","author":"L Chen","year":"2024","unstructured":"Chen, L. et al. Energy-efficient and privacy-preserved incentive mechanism for mobile edge computing-assisted federated learning in healthcare system. IEEE Trans. Mobile Comput. 23(7), 8234\u20138247. https:\/\/doi.org\/10.1109\/TMC.2024.3423567 (2024).","journal-title":"IEEE Transactions on Mobile Computing"},{"issue":"18","key":"50882_CR17","doi-asserted-by":"publisher","first-page":"29834","DOI":"10.1109\/JIOT.2024.3421045","volume":"11","author":"H Liu","year":"2024","unstructured":"Liu, H. et al. Real-time resource management mechanism for mobile iot devices in 5g-enabled edge computing. IEEE Internet of Things J. 11(18), 29834\u201329847. https:\/\/doi.org\/10.1109\/JIOT.2024.3421045 (2024).","journal-title":"IEEE Internet of Things Journal"},{"issue":"3","key":"50882_CR18","doi-asserted-by":"publisher","first-page":"24441","DOI":"10.1016\/j.heliyon.2024.e24441","volume":"10","author":"P Singh","year":"2024","unstructured":"Singh, P. et al. Fedhealthfog: A federated learning-enabled approach towards healthcare analytics over fog computing platform. Heliyon 10(3), 24441. https:\/\/doi.org\/10.1016\/j.heliyon.2024.e24441 (2024).","journal-title":"Heliyon"},{"key":"50882_CR19","doi-asserted-by":"publisher","first-page":"5025","DOI":"10.3390\/s21155025","volume":"21","author":"MU Alam","year":"2021","unstructured":"Alam, M. U. & Rahmani, R. Federated semi-supervised multi-task learning to detect covid-19 and lungs segmentation marking using chest radiography images and raspberry pi devices: An internet of medical things application. Sensors 21, 5025 (2021).","journal-title":"Sensors"},{"key":"50882_CR20","doi-asserted-by":"publisher","first-page":"3728","DOI":"10.3390\/s22103728","volume":"22","author":"T-T Ho","year":"2022","unstructured":"Ho, T.-T., Tran, K.-D. & Huang, Y. Fedsgdcovid: Federated sgd covid-19 detection under local differential privacy using chest x-ray images and symptom information. Sensors 22, 3728 (2022).","journal-title":"Sensors"},{"key":"50882_CR21","first-page":"60","volume":"4","author":"Q Dou","year":"2021","unstructured":"Dou, Q. et al. Federated deep learning for detecting covid-19 lung abnormalities in ct: a privacy-preserving multinational validation study. Dig. Med. 4, 60 (2021).","journal-title":"Digital Medicine"},{"key":"50882_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.892499","volume":"10","author":"R Durga","year":"2022","unstructured":"Durga, R. & Poovammal, E. Fled-block: Federated learning ensembled deep learning blockchain model for covid-19 prediction. Front. Public Health 10, 892499 (2022).","journal-title":"Frontiers in Public Health"},{"key":"50882_CR23","doi-asserted-by":"publisher","first-page":"710","DOI":"10.3390\/electronics12030710","volume":"12","author":"DR Kandati","year":"2023","unstructured":"Kandati, D. R. & Gadekallu, T. R. Federated learning approach for early detection of chest lesion caused by covid-19 infection using particle swarm optimization. Electronics 12, 710 (2023).","journal-title":"Electronics"},{"key":"50882_CR24","doi-asserted-by":"crossref","unstructured":"Kumar, V. Analysis of CNN features with multiple machine learning classifiers in diagnosis of monkeypox from digital skin images. MedRxiv (2022).","DOI":"10.1101\/2022.09.11.22278797"},{"key":"50882_CR25","doi-asserted-by":"publisher","first-page":"4422","DOI":"10.3390\/ijerph20054422","volume":"20","author":"AS Jaradat","year":"2023","unstructured":"Jaradat, A. S. et al. Automated monkeypox skin lesion detection using deep learning and transfer learning techniques. Int. J. Environ. Res. Public Health 20, 4422 (2023).","journal-title":"International Journal of Environmental Research Public Health"},{"key":"50882_CR26","doi-asserted-by":"publisher","first-page":"3614","DOI":"10.3390\/math10193614","volume":"10","author":"AA Abdelhamid","year":"2022","unstructured":"Abdelhamid, A. A. et al. Classification of monkeypox images based on transfer learning and the al-biruni earth radius optimization algorithm. Mathematics 10, 3614 (2022).","journal-title":"Mathematics"},{"key":"50882_CR27","doi-asserted-by":"crossref","unstructured":"Kundu, D. et al. Federated deep learning for monkeypox disease detection on gan-augmented dataset. IEEE Access (2024).","DOI":"10.1109\/ACCESS.2024.3370838"},{"key":"50882_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00305-w","volume":"1","author":"MM Islam","year":"2020","unstructured":"Islam, M. M. et al. Breast cancer prediction: A comparative study using machine learning techniques. SN Comput. Sci. 1, 1\u201314 (2020).","journal-title":"SN Computer Science"},{"key":"50882_CR29","doi-asserted-by":"publisher","first-page":"32631","DOI":"10.1038\/s41598-025-98523-w","volume":"15","author":"M Shahzad","year":"2025","unstructured":"Shahzad, M. et al. Transformer-inspired training principles based breast cancer prediction and classification through histopathology image analysis. Sci. Rep. 15, 32631. https:\/\/doi.org\/10.1038\/s41598-025-98523-w (2025).","journal-title":"Scientific Reports"},{"key":"50882_CR30","unstructured":"Agustian, F. & Lubis, M. D. I. Breast cancer detection using support vector machine and convolutional neural network. In: 2020 8th International Conference on Cyber and IT Service Management (CITSM), pp. 1\u20136. IEEE, Bengkulu, Indonesia (2020)."},{"key":"50882_CR31","doi-asserted-by":"crossref","unstructured":"Afolayan, J. O., Adebiyi, M. O., Arowolo, M. O., Chakraborty, C. & Adebiyi, A. A. Machine learning methods for breast cancer diagnosis. In Intell. Healthcare Infrastruct. 61\u201383 (Springer, Singapore, 2022).","DOI":"10.1007\/978-981-16-8150-9_4"},{"key":"50882_CR32","doi-asserted-by":"crossref","unstructured":"Kumbhare, S., Kathole, A. B. & Shinde, S. J. B. S. P. Federated learning aided breast cancer detection with intelligent heuristic-based deep learning framework. Control 86, 105080 (2023).","DOI":"10.1016\/j.bspc.2023.105080"},{"issue":"2","key":"50882_CR33","doi-asserted-by":"publisher","first-page":"60600","DOI":"10.32604\/cmc.2025.060600","volume":"83","author":"W Chen","year":"2025","unstructured":"Chen, W. et al. Ultrasegnet: A hybrid deep learning framework for breast ultrasound image segmentation. Comput. Mater. Continua 83(2), 60600. https:\/\/doi.org\/10.32604\/cmc.2025.060600 (2025).","journal-title":"Computers, Materials & Continua"},{"key":"50882_CR34","doi-asserted-by":"publisher","first-page":"1572433","DOI":"10.3389\/frai.2025.1572433","volume":"8","author":"X Li","year":"2025","unstructured":"Li, X. et al. Hma-net: a hybrid mixer framework with multihead attention for breast ultrasound image segmentation. Front. Artif. Intell. 8, 1572433. https:\/\/doi.org\/10.3389\/frai.2025.1572433 (2025).","journal-title":"Frontiers in Artificial Intelligence"},{"key":"50882_CR35","doi-asserted-by":"publisher","first-page":"25229","DOI":"10.1038\/s41598-024-77196-x","volume":"14","author":"A Almaliki","year":"2024","unstructured":"Almaliki, A. et al. Advanced federated ensemble internet of learning things based smart healthcare system. Sci. Rep. 14, 25229. https:\/\/doi.org\/10.1038\/s41598-024-77196-x (2024).","journal-title":"Scientific Reports"},{"key":"50882_CR36","doi-asserted-by":"publisher","unstructured":"Group A.I.R,. Hybrid segmentation and 3d imaging: Comprehensive framework for breast cancer. Biomed. Eng. Comput. 62, 1504. https:\/\/doi.org\/10.1016\/j.bbe.2024.10.504 (2024).","DOI":"10.1016\/j.bbe.2024.10.504"},{"issue":"3","key":"50882_CR37","first-page":"63972","volume":"144","author":"V Kumar","year":"2024","unstructured":"Kumar, V. et al. Secure malicious node detection in decentralized healthcare networks using cloud and edge computing with blockchain-enabled federated learning. Comput. Model. Eng. Sci. 144(3), 63972 (2024).","journal-title":"Computer Modeling in Engineering & Sciences"},{"key":"50882_CR38","first-page":"8723","volume":"19","author":"Q Wang","year":"2024","unstructured":"Wang, Q. et al. Hiersfl: Local differential privacy-aided split federated learning in mobile edge computing. IEEE Trans. Inf. Forensics Secur. 19, 8723\u20138735 (2024).","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"50882_CR39","unstructured":"MOONEY, P. Breast Histopathology Images. kaggle dataset, vol. 2022 (2018)."},{"key":"50882_CR40","doi-asserted-by":"crossref","unstructured":"Bala, D. Hossain: Medical image classification using neural networks. In: Elsevier (ed.) Neural Networks vol. 161, pp. 757\u2013775 (2023).","DOI":"10.1016\/j.neunet.2023.02.022"},{"key":"50882_CR41","unstructured":"Clark, A. F. Medical Image Processing Methods (2012)."},{"key":"50882_CR42","unstructured":"WHO: World Health Organization Guidelines. vol. 2024 (2023)."},{"key":"50882_CR43","doi-asserted-by":"crossref","unstructured":"Mannan, N. et al. Detection of sars-cov-2 rna by reverse transcription-polymerase chain reaction (rt-pcr) on self-collected nasal swab compared with professionally collected nasopharyngeal swab. Cureus 14 (2022).","DOI":"10.7759\/cureus.25618"},{"key":"50882_CR44","doi-asserted-by":"crossref","unstructured":"Rahman, M. T. et al. Zoonotic diseases: Etiology, impact, and control. Microorganisms 8 (2020).","DOI":"10.3390\/microorganisms8091405"},{"key":"50882_CR45","unstructured":"WHO: Monkeypox Outbreak Response Webinar. 23 July 2022 (2022)."},{"key":"50882_CR46","unstructured":"Breastcancer.org: Breast Cancer Statistics and Information. December 8, 2023 (2023)."},{"key":"50882_CR47","doi-asserted-by":"publisher","unstructured":"Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. International conference on learning representations (ICLR) https:\/\/doi.org\/10.48550\/arXiv.1409.1556 (2015)","DOI":"10.48550\/arXiv.1409.1556"},{"key":"50882_CR48","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. & Rabinovich, A. Going deeper with convolutions. In: proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"50882_CR49","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/IOTM.004.2100182","volume":"5","author":"T Zhang","year":"2022","unstructured":"Zhang, T. et al. Federated learning for the internet of things: Applications, challenges, and opportunities. IEEE Internet of Things Mag. 5, 24\u201329 (2022).","journal-title":"IEEE Internet of Things Magazine"},{"key":"50882_CR50","first-page":"119","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke, N. et al. The future of digital health with federated learning. Dig. Med. 3, 119 (2020).","journal-title":"Digital Medicine"},{"key":"50882_CR51","doi-asserted-by":"publisher","unstructured":"Mironov, I. R\u00e9nyi differential privacy. In: 2017 IEEE 30th Computer Security Foundations Symposium (CSF), pp. 263\u2013275. https:\/\/doi.org\/10.1109\/CSF.2017.11 . IEEE. https:\/\/ieeexplore.ieee.org\/document\/8049725 (2017)","DOI":"10.1109\/CSF.2017.11"},{"key":"50882_CR52","doi-asserted-by":"crossref","unstructured":"Riedel, P., Schwerin, R., Schaudt, D., Hafner, A. & Sp\u00e4te, C. Resnetfed: Federated deep learning architecture for privacy-preserving pneumonia detection from covid-19 chest radiographs. J. Healthcare Inf. Res.1\u201322 (2023).","DOI":"10.1007\/s41666-023-00132-7"},{"key":"50882_CR53","unstructured":"Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A. & Smith, V. Federated optimization in heterogeneous networks. In: Proceedings of Machine Learning and Systems, vol. 2, pp. 429\u2013450 (2020)."},{"key":"50882_CR54","unstructured":"Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S. U. & Suresh, A. T. Scaffold: Stochastic controlled averaging for federated learning. In: International Conference on Machine Learning, pp. 5132\u20135143. PMLR (2020)."},{"key":"50882_CR55","first-page":"7611","volume":"33","author":"J Wang","year":"2020","unstructured":"Wang, J., Liu, Q., Liang, H., Joshi, G. & Poor, H. V. Tackling the objective inconsistency problem in heterogeneous federated optimization. Adv. Neural Inf. Process.  Syst. 33, 7611\u20137623 (2020).","journal-title":"Advances in neural information processing systems"},{"key":"50882_CR56","doi-asserted-by":"publisher","first-page":"16301","DOI":"10.1109\/JSEN.2021.3076767","volume":"21","author":"R Kumar","year":"2021","unstructured":"Kumar, R. et al. Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging. IEEE Sens. J. 21, 16301\u201316314 (2021).","journal-title":"IEEE Sensors Journal"},{"key":"50882_CR57","doi-asserted-by":"crossref","unstructured":"Dong, N., Kampffmeyer, M., Voiculescu, I. & Xing, E. Federated partially supervised learning with limited decentralized medical images. IEEE Trans. Med. Imag. (2022).","DOI":"10.1109\/TMI.2022.3231017"},{"key":"50882_CR58","unstructured":"Wang, J., Liu, Q., Liang, H., Joshi, G. & Poor, H. V. Tackling the objective inconsistency problem in heterogeneous federated optimization. arXiv preprint arXiv:2007.07481 (2020)."},{"key":"50882_CR59","unstructured":"Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Kone\u010dn\u1ef3, J., Kumar, S. & McMahan, H. B. Adaptive federated optimization. arXiv preprint arXiv:2003.00295 (2021)."},{"issue":"8","key":"50882_CR60","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1007\/s10654-020-00660-1","volume":"35","author":"R Chowdhury","year":"2020","unstructured":"Chowdhury, R. et al. Long-term strategies to control covid-19 in low and middle-income countries: an options overview of community-based, non-pharmacological interventions. Eur. J. Epidemiol. 35(8), 743\u2013748. https:\/\/doi.org\/10.1007\/s10654-020-00660-1 (2020).","journal-title":"European Journal of Epidemiology"},{"key":"50882_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.nmni.2024.101487","volume":"62","author":"P Singh","year":"2024","unstructured":"Singh, P. et al. The resurgence of monkeypox: Epidemiology, clinical features, and public health implications in the post-smallpox eradication era. New Microbes New Infect. 62, 101487. https:\/\/doi.org\/10.1016\/j.nmni.2024.101487 (2024).","journal-title":"New Microbes and New Infections"},{"key":"50882_CR62","doi-asserted-by":"publisher","DOI":"10.7937\/K9\/TCIA.2016.7O02S9CY","author":"RS Lee","year":"2016","unstructured":"Lee, R. S., Gimenez, F., Hoogi, A. & Rubin, D. Curated breast imaging subset of digital database for screening mammography (CBIS-DDSM). Cancer Imag. Arch. https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.7O02S9CY (2016).","journal-title":"The Cancer Imaging Archive"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50882-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50882-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50882-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:48:16Z","timestamp":1778690896000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-50882-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,11]]},"references-count":62,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["50882"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-50882-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,11]]},"assertion":[{"value":"22 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2026","order":3,"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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors confirm that this work complies with ethical standards and applicable regulations. This study utilizes publicly available datasets, specifically the COVID-19 image data, Monkeypox image data, and Breast Cancer Wisconsin (Diagnostic) data, all of which were collected and shared according to relevant ethical guidelines and regulations as verified by their respective providers: The COVID-19 dataset was made available by Paul Timothy Mooney on Kaggle for research purposes and has been widely used in the academic community for developing diagnostic algorithms. The Monkeypox dataset, also publicly accessible on Kaggle, was provided by Nafin Hossain and contains anonymized dermatological images suitable for machine learning research. The Breast Cancer Wisconsin dataset can be accessed through the UCI Machine Learning Repository and represents one of the most established benchmarks in medical machine learning research. Since this study relies exclusively on publicly available data and does not involve direct interaction with human participants, no new experiments were conducted involving human participants or their data. The research methodology focuses on algorithmic development and performance evaluation using existing anonymized datasets. Consequently, no additional institutional review board approval or informed consent procedures were necessary. The federated learning framework developed in this study ensures that even when applied to new datasets in clinical settings, patient privacy is preserved through the fundamental design principle that raw medical data never leaves the local healthcare institution. This approach aligns with current privacy regulations including HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation) requirements for medical data handling. All experimental procedures were conducted in accordance with the Declaration of Helsinki principles for ethical medical research, adapted for computational studies using publicly available medical imaging data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All authors consent to the publication of this manuscript and confirm that the work presented herein has not been published previously and is not under consideration for publication elsewhere.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"14734"}}