{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T20:36:12Z","timestamp":1785875772807,"version":"3.56.0"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T00:00:00Z","timestamp":1745798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"European Union-NextGenerationEU, Through the National Recovery and Resilience Plan of the Republic of Bulgaria","award":["BG-RRP-2.013-0001-C01"],"award-info":[{"award-number":["BG-RRP-2.013-0001-C01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10586-024-05014-0","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T11:25:07Z","timestamp":1745839507000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Exploring the implementation of federated learning in healthcare: a comprehensive review"],"prefix":"10.1007","volume":"28","author":[{"given":"Amjad","family":"Hudaib","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nadim","family":"Obeid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amjad","family":"Albashayreh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hebah","family":"Mosleh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yahya","family":"Tashtoush","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgi","family":"Hristov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,28]]},"reference":[{"key":"5014_CR1","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen, D., Wu, G., Suk, H.-I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221\u2013248 (2017)","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"5014_CR2","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B., S\u00e1nchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"5014_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.107977","volume":"170","author":"N Karthikeyan","year":"2024","unstructured":"Karthikeyan, N., et al.: A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease. Comput. Biol. Med. 170, 107977 (2024)","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"5014_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0217-0","volume":"6","author":"S Dash","year":"2019","unstructured":"Dash, S., Shakyawar, S.K., Sharma, M., Kaushik, S.: Big data in healthcare: management, analysis and future prospects. J. Big Data 6(1), 1\u201325 (2019)","journal-title":"J. Big Data"},{"issue":"19","key":"5014_CR5","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5102","volume":"31","author":"B Liu","year":"2019","unstructured":"Liu, B., Ding, M., Zhu, T., Xiang, Y., Zhou, W.: Adversaries or allies? Privacy and deep learning in big data era. Concurrency Comput: Practice Exp. 31(19), e5102 (2019)","journal-title":"Concurrency Comput: Practice Exp."},{"key":"5014_CR6","doi-asserted-by":"crossref","unstructured":"Agrawal, S., Chowdhuri, A., Sarkar, S., Selvanambi, R., Gadekallu, T.\u00a0R. et\u00a0al.: Temporal weighted averaging for asynchronous federated intrusion detection systems. Comput. Intell. Neurosci. 2021 (2021)","DOI":"10.1155\/2021\/5844728"},{"key":"5014_CR7","unstructured":"Chen, M., Mathews, R., Ouyang, T., Beaufays, F.: Federated learning of out-of-vocabulary words (2019). arXiv preprint arXiv:1903.10635"},{"key":"5014_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41666-020-00082-4","volume":"5","author":"J Xu","year":"2021","unstructured":"Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J., Wang, F.: Federated learning for healthcare informatics. J. Healthcare Inf. Res. 5, 1\u201319 (2021)","journal-title":"J. Healthcare Inf. Res."},{"key":"5014_CR9","first-page":"74","volume":"2019","author":"RU Khan","year":"2019","unstructured":"Khan, R.U., Zhang, X., Alazab, M., Kumar, R.: An improved convolutional neural network model for intrusion detection in networks. Cybersecur. Cyberforens. Conf. 2019, 74\u201377 (2019)","journal-title":"Cybersecur. Cyberforens. Conf."},{"key":"5014_CR10","first-page":"1","volume":"526","author":"M Alazab","year":"2016","unstructured":"Alazab, M., Broadhurst, R.: Spam and criminal activity. Trends Iss. Crime Criminal Justice 526, 1\u201320 (2016)","journal-title":"Trends Iss. Crime Criminal Justice"},{"key":"5014_CR11","unstructured":"Hill, P.: The rationale for learning communities and learning community models (1985)"},{"key":"5014_CR12","volume-title":"Learning communities","author":"K Kellogg","year":"1999","unstructured":"Kellogg, K.: Learning communities. Eric Digest (1999)"},{"issue":"2","key":"5014_CR13","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/JBHI.2022.3181823","volume":"27","author":"M Ali","year":"2022","unstructured":"Ali, M., Naeem, F., Tariq, M., Kaddoum, G.: Federated learning for privacy preservation in smart healthcare systems: a comprehensive survey. IEEE J. Biomed. Health Inform. 27(2), 778\u2013789 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"5014_CR14","unstructured":"Mammen, P.\u00a0M.: Federated learning: opportunities and challenges (2021). arXiv preprint arXiv:2101.05428"},{"key":"5014_CR15","first-page":"1273","volume-title":"Artificial intelligence and statistics","author":"B McMahan","year":"2017","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.Y.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"5014_CR16","doi-asserted-by":"publisher","unstructured":"Alzu\u2019bi, A., Al-Hadhrami, T., Albashayreh, A., Younis, L.B.: Automatic gesture-based Arabic sign language recognition: a federated learning approach. Nafath 9 (10) (2024). https:\/\/doi.org\/10.54455\/MCN2703","DOI":"10.54455\/MCN2703"},{"key":"5014_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107318","volume":"229","author":"A Jim\u00e9nez-S\u00e1nchez","year":"2023","unstructured":"Jim\u00e9nez-S\u00e1nchez, A., Tardy, M., Ballester, M.A.G., Mateus, D., Piella, G.: Memory-aware curriculum federated learning for breast cancer classification. Comput. Methods Programs Biomed. 229, 107318 (2023)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"2","key":"5014_CR18","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1111\/coin.12563","volume":"39","author":"AAV Subramanian","year":"2023","unstructured":"Subramanian, A.A.V., Venugopal, J.P.: A deep ensemble network model for classifying and predicting breast cancer. Comput. Intell. 39(2), 258\u2013282 (2023)","journal-title":"Comput. Intell."},{"key":"5014_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102572","volume":"141","author":"A Heidari","year":"2023","unstructured":"Heidari, A., Javaheri, D., Toumaj, S., Navimipour, N.J., Rezaei, M., Unal, M.: A new lung cancer detection method based on the chest CT images using federated learning and blockchain systems. Artif. Intell. Med. 141, 102572 (2023)","journal-title":"Artif. Intell. Med."},{"key":"5014_CR20","unstructured":"Beguier, C., Terrail, J.O.d., Meah, I., Andreux, M., Tramel, E.\u00a0W.: Differentially private federated learning for cancer prediction (2021). arXiv preprint arXiv:2101.02997"},{"key":"5014_CR21","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.radonc.2022.09.023","volume":"176","author":"CR Hansen","year":"2022","unstructured":"Hansen, C.R., Price, G., Field, M., Sarup, N., Zukauskaite, R., Johansen, J., Eriksen, J.G., Aly, F., McPartlin, A., Holloway, L., et al.: Larynx cancer survival model developed through open-source federated learning. Radiother. Oncol. 176, 179\u2013186 (2022)","journal-title":"Radiother. Oncol."},{"key":"5014_CR22","doi-asserted-by":"crossref","unstructured":"AlSalman, H., Al-Rakhami, M.S., Alfakih, T., Hassan, M.M.: Federated learning approach for breast cancer detection based on DCNN. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3374650"},{"issue":"1","key":"5014_CR23","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1038\/s41597-023-02100-7","volume":"10","author":"HT Nguyen","year":"2023","unstructured":"Nguyen, H.T., Nguyen, H.Q., Pham, H.H., Lam, K., Le, L.T., Dao, M., Vu, V.: Vindr-mammo: a large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography. Sci. Data 10(1), 277 (2023)","journal-title":"Sci. Data"},{"key":"5014_CR24","unstructured":"Cui, C., Li, L., Cai, H., Fan, Z., Zhang, L., Dan, T., Li, J., Wang, J.: The Chinese mammography database (CMMD): an online mammography database with biopsy confirmed types for machine diagnosis of breast. Cancer Imaging Archive 1 (2021)"},{"issue":"2","key":"5014_CR25","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","volume":"19","author":"IC Moreira","year":"2012","unstructured":"Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: Inbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236\u2013248 (2012)","journal-title":"Acad. Radiol."},{"key":"5014_CR26","first-page":"1362","volume-title":"44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","author":"SIA Meerza","year":"2022","unstructured":"Meerza, S.I.A., Li, Z., Liu, L., Zhang, J., Liu, J.: Fair and privacy-preserving Alzheimer\u2019s disease diagnosis based on spontaneous speech analysis via federated learning. In: 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1362\u20131365. IEEE (2022)"},{"issue":"19","key":"5014_CR27","doi-asserted-by":"publisher","first-page":"8272","DOI":"10.3390\/s23198272","volume":"23","author":"K Khalil","year":"2023","unstructured":"Khalil, K., Khan Mamun, M.M.R., Sherif, A., Elsersy, M.S., Imam, A.A.-A., Mahmoud, M., Alsabaan, M.: A federated learning model based on hardware acceleration for the early detection of Alzheimer\u2019s disease. Sensors 23(19), 8272 (2023)","journal-title":"Sensors"},{"key":"5014_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110804","volume":"147","author":"A Lakhan","year":"2023","unstructured":"Lakhan, A., Gr\u00f8nli, T.-M., Muhammad, G., Tiwari, P.: Edcnns: federated learning enabled evolutionary deep convolutional neural network for Alzheimer disease detection. Appl. Soft Comput. 147, 110804 (2023)","journal-title":"Appl. Soft Comput."},{"issue":"7","key":"5014_CR29","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1080\/13682199.2023.2172524","volume":"70","author":"U Mandawkar","year":"2022","unstructured":"Mandawkar, U., Diwan, T.: Alzheimer disease classification using tawny flamingo based deep convolutional neural networks via federated learning. Imaging Sci. J. 70(7), 459\u2013472 (2022)","journal-title":"Imaging Sci. J."},{"issue":"14","key":"5014_CR30","doi-asserted-by":"publisher","first-page":"16301","DOI":"10.1109\/JSEN.2021.3076767","volume":"21","author":"R Kumar","year":"2021","unstructured":"Kumar, R., Khan, A.A., Kumar, J., Golilarz, N.A., Zhang, S., Ting, Y., Zheng, C., Wang, W., et al.: Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging. IEEE Sens. J. 21(14), 16301\u201316314 (2021)","journal-title":"IEEE Sens. J."},{"key":"5014_CR31","unstructured":"Khan, A.: COVID-19 CT scan dataset. https:\/\/github.com\/abdkhanstd\/COVID-19. Accessed 9 April 2024"},{"key":"5014_CR32","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/ICIRCA57980.2023.10220772","volume-title":"2023 5th international conference on inventive research in computing applications (ICIRCA)","author":"P Pravallika","year":"2023","unstructured":"Pravallika, P., Gnanasri, V., Gireesh, M., Reddy, A.A., Kalpana, V., Chowdary, J.S.: Enhanced COVID-19 detection and privacy preserving using federated learning. In: 2023 5th international conference on inventive research in computing applications (ICIRCA), pp. 301\u2013305. IEEE (2023)"},{"key":"5014_CR33","unstructured":"Soares, E., Angelov, P., Biaso, S., Froes, M.H., Abe, D.K.: SARS-COV-2 CT-scan dataset: a large dataset of real patients CT scans for SARS-COV-2 identification. MedRxiv (2020) 2020\u201304"},{"issue":"5","key":"5014_CR34","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13173","volume":"40","author":"D Chowdhury","year":"2023","unstructured":"Chowdhury, D., Banerjee, S., Sannigrahi, M., Chakraborty, A., Das, A., Dey, A., Dwivedi, A.D.: Federated learning based COVID-19 detection. Expert. Syst. 40(5), e13173 (2023)","journal-title":"Expert. Syst."},{"key":"5014_CR35","doi-asserted-by":"publisher","unstructured":"Sheet, D., Chakravarty, A., Sarkar, T., Sathish, R., Raj, A., Balasubramanian, V., Rajan, R., Sathish, R., Chakravorty, N., Sinha, M., Sharma, M., Kumar, V., Kumar, R., Kumar, A., Singhal, A., Reddy, G.: Covid19 action-radiology-CXR (2020). https:\/\/doi.org\/10.21227\/s7pw-jr18","DOI":"10.21227\/s7pw-jr18"},{"key":"5014_CR36","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1109\/AIC57670.2023.10263905","volume-title":"2023 IEEE world conference on applied intelligence and computing (AIC)","author":"A Pathak","year":"2023","unstructured":"Pathak, A., Sharma, S., Singh, A., Pandey, S., Yamsani, N., Singh, R.: A study of deep learning and blockchain-federated learning models for COVID-19 identification utilizing CT imaging. In: 2023 IEEE world conference on applied intelligence and computing (AIC), pp. 562\u2013568. IEEE (2023)"},{"key":"5014_CR37","doi-asserted-by":"publisher","first-page":"92681","DOI":"10.1109\/ACCESS.2022.3202922","volume":"10","author":"A Giuseppi","year":"2022","unstructured":"Giuseppi, A., Manfredi, S., Menegatti, D., Poli, C., Pietrabissa, A.: Decentralised federated learning for hospital networks with application to COVID-19 detection. IEEE Access 10, 92681\u201392691 (2022)","journal-title":"IEEE Access"},{"key":"5014_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104194","volume":"134","author":"C Sun","year":"2022","unstructured":"Sun, C., van Soest, J., Koster, A., Eussen, S.J., Schram, M.T., Stehouwer, C.D., Dagnelie, P.C., Dumontier, M.: Studying the association of diabetes and healthcare cost on distributed data from the Maastricht study and statistics Netherlands using a privacy-preserving federated learning infrastructure. J. Biomed. Inform. 134, 104194 (2022)","journal-title":"J. Biomed. Inform."},{"key":"5014_CR39","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1109\/BDICN55575.2022.00095","volume-title":"2022 international conference on big data, information and computer network (BDICN)","author":"J Liu","year":"2022","unstructured":"Liu, J., Lu, X., Yang, H., Zhuang, L.: A diabetes prediction system based on federated learning. In: 2022 international conference on big data, information and computer network (BDICN), pp. 486\u2013491. IEEE (2022)"},{"issue":"4","key":"5014_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.xops.2021.100069","volume":"1","author":"J Lo","year":"2021","unstructured":"Lo, J., Timothy, T.Y., Ma, D., Zang, P., Owen, J.P., Zhang, Q., Wang, R.K., Beg, M.F., Lee, A.Y., Jia, Y., et al.: Federated learning for microvasculature segmentation and diabetic retinopathy classification of oct data. Ophthalmol. Sci. 1(4), 100069 (2021)","journal-title":"Ophthalmol. Sci."},{"key":"5014_CR41","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/IIT59782.2023.10366471","volume-title":"2023 15th international conference on innovations in information technology (IIT)","author":"HM Khater","year":"2023","unstructured":"Khater, H.M., Tariq, A., Sallabi, F., Serhani, M.A., Barka, E.: Federated-edge computing based cyber-physical systems framework for enhanced diabetes management. In: 2023 15th international conference on innovations in information technology (IIT), pp. 67\u201372. IEEE (2023)"},{"key":"5014_CR42","unstructured":"Tigganeha, A.: Diabetes dataset 2019 (2019). https:\/\/www.kaggle.com\/datasets\/tigganeha4\/diabetes-dataset-2019"},{"key":"5014_CR43","first-page":"315","volume-title":"23rd international carpathian control conference (ICCC)","author":"M M\u0103muleanu","year":"2022","unstructured":"M\u0103muleanu, M., Ionete, C., Albi\u0163a, A., Seli\u015fteanu, D.: Distributed deep learning model for predicting the risk of diabetes, trained on imbalanced dataset. In: 23rd international carpathian control conference (ICCC), pp. 315\u2013318. IEEE (2022)"},{"key":"5014_CR44","unstructured":"UCI machine learning repository. Pima Indians diabetes database\u2014Kaggle. https:\/\/www.kaggle.com\/datasets\/uciml\/pima-indians-diabetes-database"},{"key":"5014_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104881","volume":"85","author":"Y Su","year":"2023","unstructured":"Su, Y., Huang, C., Zhu, W., Lyu, X., Ji, F.: Multi-party diabetes mellitus risk prediction based on secure federated learning. Biomed. Signal Process. Control 85, 104881 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"5014_CR46","unstructured":"Choudhury, O., Gkoulalas-Divanis, A., Salonidis, T., Sylla, I., Park, Y., Hsu, G., Das, A.: Differential privacy-enabled federated learning for sensitive health data (2019). arXiv preprint arXiv:1910.02578"},{"key":"5014_CR47","first-page":"32","volume-title":"IEEE 35th international symposium on computer-based medical systems (CBMS)","author":"L Mondrejevski","year":"2022","unstructured":"Mondrejevski, L., Miliou, I., Montanino, A., Pitts, D., Hollm\u00e9n, J., Papapetrou, P.: Flicu: a federated learning workflow for intensive care unit mortality prediction. In: IEEE 35th international symposium on computer-based medical systems (CBMS), pp. 32\u201337. IEEE (2022)"},{"issue":"1","key":"5014_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AEW Johnson","year":"2016","unstructured":"Johnson, A.E.W., Pollard, T.J., Shen, L., Lehman, L.-W.H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L.A., Mark, R.G.: MIMIC-III, a freely accessible critical care database. Sci. Data 3(1), 1\u20139 (2016). https:\/\/doi.org\/10.1038\/sdata.2016.35","journal-title":"Sci. Data"},{"issue":"5","key":"5014_CR49","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3514500","volume":"13","author":"TK Dang","year":"2022","unstructured":"Dang, T.K., Lan, X., Weng, J., Feng, M.: Federated learning for electronic health records. ACM Trans. Intell. Syst. Technol. 13(5), 1\u201317 (2022)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"5014_CR50","doi-asserted-by":"crossref","unstructured":"Dang, T.K., Tan, K.C., Choo, M., Lim, N., Weng, J., Feng, M.: Building ICU in-hospital mortality prediction model with federated learning. Federated Learning: Privacy and Incentive, pp. 255\u2013268 (2020)","DOI":"10.1007\/978-3-030-63076-8_18"},{"issue":"3","key":"5014_CR51","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000117","volume":"2","author":"S Rajendran","year":"2023","unstructured":"Rajendran, S., Xu, Z., Pan, W., Ghosh, A., Wang, F.: Data heterogeneity in federated learning with electronic health records: case studies of risk prediction for acute kidney injury and sepsis diseases in critical care. PLOS Digital Health 2(3), e0000117 (2023)","journal-title":"PLOS Digital Health"},{"issue":"2","key":"5014_CR52","doi-asserted-by":"publisher","first-page":"970","DOI":"10.3390\/s23020970","volume":"23","author":"MU Alam","year":"2023","unstructured":"Alam, M.U., Rahmani, R.: Fedsepsis: a federated multi-modal deep learning-based internet of medical things application for early detection of sepsis from electronic health records using raspberry pi and Jetson nano devices. Sensors 23(2), 970 (2023)","journal-title":"Sensors"},{"key":"5014_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2024.102982","volume":"157","author":"C D\u00fcsing","year":"2024","unstructured":"D\u00fcsing, C., Cimiano, P., Rehberg, S., Scherer, C., Kaup, O., K\u00f6ster, C., Hellmich, S., Herrmann, D., Meier, K.L., Cla\u00dfen, S., et al.: Integrating federated learning for improved counterfactual explanations in clinical decision support systems for sepsis therapy. Artif. Intell. Med. 157, 102982 (2024)","journal-title":"Artif. Intell. Med."},{"key":"5014_CR54","doi-asserted-by":"crossref","unstructured":"Ding, R., Rong, F., Han, X., Wang, L.: Cross-center early sepsis recognition by medical knowledge guided collaborative learning for data-scarce hospitals. In: Proceedings of the ACM Web Conference, pp. 3987\u20133993 (2023)","DOI":"10.1145\/3543507.3583989"},{"key":"5014_CR55","doi-asserted-by":"crossref","unstructured":"Khan, A.\u00a0A., Alsubai, S., Wechtaisong, C., Almadhor, A., Kryvinska, N., Al\u00a0Hejaili, A., Mohammad, U.\u00a0G.: CD-FL: Cataract images based disease detection using federated learning. Comput. Syst. Sci. Eng. 47\u00a0(2) (2023)","DOI":"10.32604\/csse.2023.039296"},{"issue":"21","key":"5014_CR56","doi-asserted-by":"publisher","first-page":"11620","DOI":"10.3390\/app132111620","volume":"13","author":"T Baptista","year":"2023","unstructured":"Baptista, T., Soares, C., Oliveira, T., Soares, F.: Federated learning for computer-aided diagnosis of glaucoma using retinal fundus images. Appl. Sci. 13(21), 11620 (2023)","journal-title":"Appl. Sci."},{"issue":"4","key":"5014_CR57","doi-asserted-by":"publisher","first-page":"948","DOI":"10.3390\/biomedinformatics3040058","volume":"3","author":"M Chetoui","year":"2023","unstructured":"Chetoui, M., Akhloufi, M.A.: Federated learning for diabetic retinopathy detection using vision transformers. BioMedInformatics 3(4), 948\u2013961 (2023)","journal-title":"BioMedInformatics"},{"key":"5014_CR58","unstructured":"Yao, Z., Nguyen, H., Srivastava, A., Ambite, J.\u00a0L.: Task-agnostic federated learning (2024). arXiv preprint arXiv:2406.17235"},{"key":"5014_CR59","unstructured":"Raj, S.: Processed data and scripts for analyses. https:\/\/github.com\/surajraj99\/Data-Heterogeneity-in-Federated-Learning"},{"key":"5014_CR60","doi-asserted-by":"publisher","DOI":"10.13026\/rrgf-xw32","author":"AEW Johnson","year":"2022","unstructured":"Johnson, A.E.W., Bulgarelli, L., Pollard, T.J., Horng, S., Celi, L.A., Mark, R.G.: Mimic-IV (2022). https:\/\/doi.org\/10.13026\/rrgf-xw32","journal-title":"Mimic-IV"},{"key":"5014_CR61","first-page":"1","volume-title":"In: 2019 computing in cardiology (CinC)","author":"MA Reyna","year":"2019","unstructured":"Reyna, M.A., Josef, C., Seyedi, S., Jeter, R., Shashikumar, S.P., Westover, M.B., Sharma, A., Nemati, S., Clifford, G.D.: Early prediction of sepsis from clinical data: the physionet\/computing in cardiology challenge 2019. In: 2019 computing in cardiology (CinC), p. 1. IEEE (2019)"},{"key":"5014_CR62","unstructured":"Mvd, A.: Ocular disease recognition (odir-5k) (2020). https:\/\/www.kaggle.com\/datasets\/andrewmvd\/ocular-disease-recognition-odir5k. Accessed 8 Nov 2024"},{"issue":"23","key":"5014_CR63","doi-asserted-by":"publisher","first-page":"11191","DOI":"10.3390\/app112311191","volume":"11","author":"C-R Prayitno","year":"2021","unstructured":"Prayitno, Shyu, C.-R., Putra, K.T.., Chen, H.-C., Tsai, Y.-Y., Hossain, K.T., Jiang, W., Shae, Z.-Y: A systematic review of federated learning in the healthcare area: from the perspective of data properties and applications. Appl. Sci. 11(23), 11191 (2021)","journal-title":"Appl. Sci."},{"key":"5014_CR64","first-page":"2","volume":"1","author":"A Asraf","year":"2021","unstructured":"Asraf, A., Islam, Z.: Covid19, pneumonia and normal chest X-ray pa dataset. Mendeley Data 1, 2 (2021)","journal-title":"Mendeley Data"},{"key":"5014_CR65","doi-asserted-by":"crossref","unstructured":"Chai, D., Wang, L., Yang, L., Zhang, J., Chen, K., Yang, Q.: A survey for federated learning evaluations: goals and measures. IEEE Trans. Knowl. Data Eng. (2024)","DOI":"10.1109\/TKDE.2024.3382002"},{"issue":"2","key":"5014_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Mining Knowl. Manage. Process 5(2), 1 (2015)","journal-title":"Int. J. Data Mining Knowl. Manage. Process"},{"issue":"3","key":"5014_CR67","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.1007\/s10586-022-03706-z","volume":"26","author":"O Darwish","year":"2023","unstructured":"Darwish, O., Tashtoush, Y., Bashayreh, A., Alomar, A., Alkhaza\u2019leh, S., Darweesh, D.: A survey of uncover misleading and cyberbullying on social media for public health. Clust. Comput. 26(3), 1709\u20131735 (2023)","journal-title":"Clust. Comput."},{"key":"5014_CR68","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.y.: Communication-efficient learning of deep networks from decentralized data. In: A.\u00a0Singh, J.\u00a0Zhu (Eds.), Proceedings of the 20th international conference on artificial intelligence and statistics, Vol.\u00a054 of Proceedings of machine learning research, pp. 1273\u20131282, PMLR (2017). https:\/\/proceedings.mlr.press\/v54\/mcmahan17a.html"},{"key":"5014_CR69","unstructured":"Sahu, A.\u00a0K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., Smith, V.: On the convergence of federated optimization in heterogeneous networks. CoRR abs\/1812.06127 (2018). arxiv:1812.06127"},{"key":"5014_CR70","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-IID data. CoRR abs\/1806.00582 (2018). arxiv:1806.00582"},{"key":"5014_CR71","unstructured":"Geiping, J., Bauermeister, H., Dr\u00f6ge, H., Moeller, M.: Inverting gradients: how easy is it to break privacy in federated learning? In: Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS '20). Curran Associates Inc., Red Hook, NY, USA, pp. 16937\u201316947 (2020)"},{"key":"5014_CR72","doi-asserted-by":"crossref","unstructured":"Yin, H., Mallya, A., Vahdat, A., Alvarez, J.M., Kautz, J., Molchanov, P.: See through gradients: Image batch recovery via gradinversion (2021). arXiv:2104.07586","DOI":"10.1109\/CVPR46437.2021.01607"},{"key":"5014_CR73","unstructured":"Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. CoRR abs\/1906.08935 (2019). arxiv:1906.08935"},{"key":"5014_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2024.103744","volume":"139","author":"W Chang","year":"2024","unstructured":"Chang, W., Zhu, T.: Gradient-based defense methods for data leakage in vertical federated learning. Comput. Secur. 139, 103744 (2024)","journal-title":"Comput. Secur."},{"key":"5014_CR75","unstructured":"Yue, K., Jin, R., Wong, C.-W., Baron, D., Dai, H.: Gradient obfuscation gives a false sense of security in federated learning. In: 32nd USENIX security symposium (USENIX Security 23), pp. 6381\u20136398 (2023)"},{"key":"5014_CR76","doi-asserted-by":"crossref","unstructured":"Hu, J., Wang, Z., Shen, Y., Lin, B., Sun, P., Pang, X., Liu, J., Ren, K.: Shield against gradient leakage attacks: adaptive privacy-preserving federated learning. IEEE\/ACM Trans. Network. (2023)","DOI":"10.1109\/TNET.2023.3317870"},{"key":"5014_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103270","volume":"130","author":"P Gupta","year":"2023","unstructured":"Gupta, P., Yadav, K., Gupta, B.B., Alazab, M., Gadekallu, T.R.: A novel data poisoning attack in federated learning based on inverted loss function. Comput. Secur. 130, 103270 (2023)","journal-title":"Comput. Secur."},{"key":"5014_CR78","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3216981","author":"L Lyu","year":"2022","unstructured":"Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., Philip, S.Y.: Privacy and robustness in federated learning: attacks and defenses. IEEE Trans. Neural Netw. Learn. Syst. (2022).\u00a0https:\/\/doi.org\/10.1109\/TNNLS.2022.3216981","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"5014_CR79","first-page":"9020","volume":"37","author":"X Lyu","year":"2023","unstructured":"Lyu, X., Han, Y., Wang, W., Liu, J., Wang, B., Liu, J., Zhang, X.: Poisoning with Cerberus: stealthy and colluded backdoor attack against federated learning. Proc AAAI Conf. Artif. Intell. 37, 9020\u20139028 (2023)","journal-title":"Proc AAAI Conf. Artif. Intell."},{"key":"5014_CR80","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.patrec.2024.01.001","volume":"178","author":"VJ Prakash","year":"2024","unstructured":"Prakash, V.J., Vijay, S.A.A.: A multi-aspect framework for explainable sentiment analysis. Pattern Recogn. Lett. 178, 122\u2013129 (2024)","journal-title":"Pattern Recogn. Lett."}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-05014-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-05014-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-05014-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T12:26:16Z","timestamp":1757161576000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-05014-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,28]]},"references-count":80,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["5014"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-05014-0","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,28]]},"assertion":[{"value":"26 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2025","order":4,"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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"302"}}