{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:19:18Z","timestamp":1780355958179,"version":"3.54.1"},"reference-count":132,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008977","name":"Universit\u00e4t Ulm","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008977","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>While Federated Learning (FL) provides a privacy-preserving approach to analyze sensitive data without centralizing training data, the field lacks an detailed comparison of emerging open-source FL frameworks. Furthermore, there is currently no standardized, weighted evaluation scheme for a fair comparison of FL frameworks that would support the selection of a suitable FL framework. This study addresses these research gaps by conducting a comparative analysis of 15 individual open-source FL frameworks filtered by two selection criteria, using the literature review methodology proposed by Webster and Watson. These framework candidates are compared using a novel scoring schema with 15 qualitative and quantitative evaluation criteria, focusing on features, interoperability, and user friendliness. The evaluation results show that the FL framework Flower outperforms its peers with an overall score of 84.75%, while Fedlearner lags behind with a total score of 24.75%. The proposed comparison suite offers valuable initial guidance for practitioners and researchers in selecting an FL framework for the design and development of FL-driven systems. In addition, the FL framework comparison suite is designed to be adaptable and extendable accommodating the inclusion of new FL frameworks and evolving requirements.<\/jats:p>","DOI":"10.1007\/s13042-024-02234-z","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T05:02:23Z","timestamp":1719550943000},"page":"5257-5278","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Comparative analysis of open-source federated learning frameworks - a literature-based survey and review"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9910-3867","authenticated-orcid":false,"given":"Pascal","family":"Riedel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"Schick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reinhold","family":"von Schwerin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manfred","family":"Reichert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Schaudt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Hafner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"2234_CR1","first-page":"1273","volume":"54","author":"HB McMahan","year":"2017","unstructured":"McMahan HB, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. J Mach Learn Res 54:1273\u20131282","journal-title":"J Mach Learn Res"},{"key":"2234_CR2","doi-asserted-by":"publisher","unstructured":"Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. 23rd ACM conference on computer and communications security (CCS 2016), 308\u2013318. https:\/\/doi.org\/10.1145\/2976749.2978318","DOI":"10.1145\/2976749.2978318"},{"key":"2234_CR3","unstructured":"Hard A, Rao K, Mathews R, Beaufays F, Augenstein S, Eichner H, Kiddon C, Ramage D (2018) Federated learning for mobile keyboard prediction arXiv:1811.03604"},{"key":"2234_CR4","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag 37:50\u201360. https:\/\/doi.org\/10.1109\/MSP.2020.2975749","journal-title":"IEEE Signal Process Mag"},{"key":"2234_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000083","volume":"14","author":"P Kairouz","year":"2021","unstructured":"Kairouz P, McMahan HB, Avent B, Bellet A, Bennis M, Bhagoji AN, Bonawitz KA, Charles Z, Cormode G, Cummings R, D\u2019Oliveira RGL, Rouayheb SE, Evans D, Gardner J, Garrett Z, Gascon A, Ghazi B, Gibbons PB, Gruteser M, Harchaoui Z, He C, He L, Huo Z, Hutchinson B, Hsu J, Jaggi M, Javidi T, Joshi G, Khodak M, Konecny J, Korolova A, Koushanfar F, Koyejo S, Lepoint T, Liu Y, Mittal P, Mohri M, Nock R, Ozgur A, Pagh R, Raykova M, Qi H, Ramage D, Raskar R, Song D, Song W, Stich SU, Sun Z, Suresh AT, Tramer F, Vepakomma P, Wang J, Xiong L, Xu Z, Yang Q, Yu FX, Yu H, Zhao S (2021) Advances and open problems in federated learning. Found Trends Mac Learn 14:1\u2013121. https:\/\/doi.org\/10.1561\/2200000083","journal-title":"Found Trends Mac Learn"},{"key":"2234_CR6","doi-asserted-by":"publisher","first-page":"3333","DOI":"10.1109\/TKDE.2021.3140131","volume":"35","author":"L Zhang","year":"2023","unstructured":"Zhang L, Zhu T, Xiong P, Zhou W, Yu P (2023) A robust game-theoretical federated learning framework with joint differential privacy. IEEE Trans Knowl Data Eng 35:3333\u20133346. https:\/\/doi.org\/10.1109\/TKDE.2021.3140131","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2234_CR7","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1109\/TPDS.2022.3225185","volume":"34","author":"H Jin","year":"2023","unstructured":"Jin H, Bai D, Yao D, Dai Y, Gu L, Yu C, Sun L (2023) Personalized edge intelligence via federated self-knowledge distillation. IEEE Trans Parallel Distrib Syst 34:567\u2013580. https:\/\/doi.org\/10.1109\/TPDS.2022.3225185","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"2234_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3453476","volume":"55","author":"DC Nguyen","year":"2022","unstructured":"Nguyen DC, Pham Q-V, Pathirana PN, Ding M, Seneviratne A, Lin Z, Dobre O, Hwang W-J (2022) Federated learning for smart healthcare: a survey. ACM Comput Surv 55:1\u201337","journal-title":"ACM Comput Surv"},{"key":"2234_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3501813","volume":"13","author":"RS Antunes","year":"2022","unstructured":"Antunes RS, da Costa CA, K\u00fcderle A, Yari IA, Eskofier B (2022) Federated learning for healthcare: systematic review and architecture proposal. ACM Trans Intell Syst Technol 13:1\u201323","journal-title":"ACM Trans Intell Syst Technol"},{"key":"2234_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3201203","volume":"71","author":"H Xing","year":"2022","unstructured":"Xing H, Xiao Z, Qu R, Zhu Z, Zhao B (2022) An efficient federated distillation learning system for multi-task time series classification. IEEE Trans Instrum Meas 71:1\u201312. https:\/\/doi.org\/10.1109\/TIM.2022.3201203","journal-title":"IEEE Trans Instrum Meas"},{"key":"2234_CR11","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s41666-023-00132-7","volume":"7","author":"P Riedel","year":"2023","unstructured":"Riedel P, von Schwerin R, Schaudt D, Hafner A, Resnetfed S (2023) Federated deep learning architecture for privacy-preserving pneumonia detection from covid-19 chest radiographs. J Healthcare Inf Res 7:203\u2013224","journal-title":"J Healthcare Inf Res"},{"key":"2234_CR12","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1007\/s10586-022-03658-4","volume":"26","author":"A Rahman","year":"2023","unstructured":"Rahman A, Hossain MS, Muhammad G, Kundu D, Debnath T, Rahman M, Khan MSI, Tiwari P, Band SS (2023) Federated learning-based ai approaches in smart healthcare: concepts, taxonomies, challenges and open issues. Clust Comput 26:2271\u20132311. https:\/\/doi.org\/10.1007\/s10586-022-03658-4","journal-title":"Clust Comput"},{"key":"2234_CR13","first-page":"19","volume":"18","author":"S Bharati","year":"2022","unstructured":"Bharati S, Mondal MRH, Podder P, Prasath VBS (2022) Federated learning: applications, challenges and future directions. Int J Hybrid Intell Syst 18:19\u201335","journal-title":"Int J Hybrid Intell Syst"},{"key":"2234_CR14","doi-asserted-by":"publisher","first-page":"3642","DOI":"10.1109\/JIOT.2022.3231363","volume":"10","author":"L Witt","year":"2023","unstructured":"Witt L, Heyer M, Toyoda K, Samek W, Li D (2023) Decentral and incentivized federated learning frameworks: a systematic literature review. IEEE Internet Things J 10:3642\u20133663","journal-title":"IEEE Internet Things J"},{"key":"2234_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107338","author":"Z Xiao","year":"2021","unstructured":"Xiao Z, Xu X, Xing H, Song F, Wang X, Zhao B (2021) A federated learning system with enhanced feature extraction for human activity recognition. Knowl-Based Syst. https:\/\/doi.org\/10.1016\/j.knosys.2021.107338","journal-title":"Knowl-Based Syst"},{"key":"2234_CR16","doi-asserted-by":"crossref","unstructured":"Boobalan P, Ramu SP, Pham QV, Dev K, Pandya S, Maddikunta PKR, Gadekallu TR, Huynh-The T (2022) Fusion of federated learning and industrial internet of things: a survey. Comput Netw 212","DOI":"10.1016\/j.comnet.2022.109048"},{"key":"2234_CR17","first-page":"2","volume":"55","author":"S Pandya","year":"2023","unstructured":"Pandya S, Srivastava G, Jhaveri R, Babu MR, Bhattacharya S, Maddikunta PKR, Mastorakis S, Thippa MJP, Gadekallu R (2023) Federated learning for smart cities: a comprehensive survey. Sustain Energy Technol Assess 55:2\u201313","journal-title":"Sustain Energy Technol Assess"},{"key":"2234_CR18","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1109\/IOTM.004.2100182","volume":"5","author":"T Zhang","year":"2022","unstructured":"Zhang T, Gao L, He C, Zhang M, Krishnamachari B, Avestimehr AS (2022) Federated learning for the internet of things: applications, challenges, and opportunities. IEEE Internet Things Mag 5:24\u201329","journal-title":"IEEE Internet Things Mag"},{"key":"2234_CR19","first-page":"1","volume":"16","author":"K Zhang","year":"2021","unstructured":"Zhang K, Song X, Zhang C, Yu S (2021) Challenges and future directions of secure federated learning: a survey. Front Comput Sci 16:1\u20138","journal-title":"Front Comput Sci"},{"key":"2234_CR20","doi-asserted-by":"crossref","unstructured":"Li C, Zeng X, Zhang M, Cao Z (2022) Pyramidfl: a fine-grained client selection framework for efficient federated learning. Proceedings of the 28th annual international conference on mobile computing and networking 28, 158\u2013171","DOI":"10.1145\/3495243.3517017"},{"key":"2234_CR21","doi-asserted-by":"crossref","unstructured":"Huang W, Ye M, Du B (2022) Learn from others and be yourself in heterogeneous federated learning. 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"2234_CR22","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s13042-022-01647-y","volume":"14","author":"J Wen","year":"2023","unstructured":"Wen J, Zhang Z, Lan Y, Cui Z, Cai J, Zhang W (2023) A survey on federated learning: challenges and applications. Int J Mach Learn Cybern 14:513\u2013535. https:\/\/doi.org\/10.1007\/s13042-022-01647-y","journal-title":"Int J Mach Learn Cybern"},{"key":"2234_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2023.103714","author":"BS Guendouzi","year":"2023","unstructured":"Guendouzi BS, Ouchani S, Assaad HE, Zaher ME (2023) A systematic review of federated learning: challenges, aggregation methods, and development tools. J Netw Comput Appl. https:\/\/doi.org\/10.1016\/j.jnca.2023.103714","journal-title":"J Netw Comput Appl"},{"key":"2234_CR24","unstructured":"Zhao Y, Li M, Lai L, Suda N, Civin D, Chandra V (2018) Federated learning with non-iid data arXiv:1806.00582"},{"key":"2234_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.100742","volume":"22","author":"ORA Almanifi","year":"2023","unstructured":"Almanifi ORA, Chow C-O, Tham M-L, Chuah JH, Kanesan J (2023) Communication and computation efficiency in federated learning: a survey. Internet Things 22:100742","journal-title":"Internet Things"},{"key":"2234_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100595","author":"C Xu","year":"2023","unstructured":"Xu C, Qu Y, Xiang Y, Gao L (2023) Asynchronous federated learning on heterogeneous devices: a survey. Comput Sci Rev. https:\/\/doi.org\/10.1016\/j.cosrev.2023.100595","journal-title":"Comput Sci Rev"},{"key":"2234_CR27","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.future.2023.09.008","volume":"150","author":"P Qi","year":"2023","unstructured":"Qi P, Chiaro D, Guzzo A, Ianni M, Fortino G, Piccialli F (2023) Model aggregation techniques in federated learning: a comprehensive survey. Futur Gener Comput Syst 150:272\u2013293. https:\/\/doi.org\/10.1016\/j.future.2023.09.008","journal-title":"Futur Gener Comput Syst"},{"key":"2234_CR28","doi-asserted-by":"crossref","unstructured":"Li Q, Diao Y, Chen Q, He B (2022) Federated learning on non-iid data silos: an experimental study. 2022 IEEE 38th iInternational conference on data engineering (ICDE)","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"2234_CR29","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TPDS.2023.3334398","volume":"35","author":"Z Wang","year":"2024","unstructured":"Wang Z, Xu H-Z, Xu Y, Jiang Z, Liu J, Chen S (2024) Fast: enhancing federated learning through adaptive data sampling and local training. IEEE Trans Parallel Distrib Syst 35:221\u2013236. https:\/\/doi.org\/10.1109\/TPDS.2023.3334398","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"2234_CR30","doi-asserted-by":"publisher","first-page":"450","DOI":"10.3390\/s22020450","volume":"22","author":"HG Abreha","year":"2022","unstructured":"Abreha HG, Hayajneh M, Serhani MA (2022) Federated learning in edge computing: a systematic survey. Sensors 22:450","journal-title":"Sensors"},{"key":"2234_CR31","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1145\/3511285.3511291","volume":"25","author":"SM Ticao Zhang","year":"2021","unstructured":"Ticao Zhang SM (2021) An introduction to the federated learning standard. GetMobile Mobile Comput Commun 25:18\u201322","journal-title":"GetMobile Mobile Comput Commun"},{"key":"2234_CR32","doi-asserted-by":"publisher","first-page":"2983","DOI":"10.1109\/COMST.2023.3315746","volume":"25","author":"ETM Beltr\u00e1n","year":"2023","unstructured":"Beltr\u00e1n ETM, P\u00e9rez MQ, S\u00e1nchez PMS, Bernal SL, Bovet G, P\u00e9rez MG, P\u00e9rez GM, Celdr\u00e1n AH (2023) Decentralized federated learning: fundamentals, state of the art, frameworks, trends, and challenges. IEEE Commun Surv Tutorials 25:2983\u20133013. https:\/\/doi.org\/10.1109\/COMST.2023.3315746","journal-title":"IEEE Commun Surv Tutorials"},{"key":"2234_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 10:1\u201319. https:\/\/doi.org\/10.1145\/3298981","journal-title":"ACM Trans Intell Syst Technol"},{"key":"2234_CR34","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/MWC.017.2100714","volume":"30","author":"X Gong","year":"2023","unstructured":"Gong X, Chen Y, Wang Q, Kong W (2023) Backdoor attacks and defenses in federated learning: state-of-the-art, taxonomy, and future directions. IEEE Wirel Commun 30:114\u2013121. https:\/\/doi.org\/10.1109\/MWC.017.2100714","journal-title":"IEEE Wirel Commun"},{"key":"2234_CR35","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1561\/0400000042","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork C, Roth A (2014) The algorithmic foundations of differential privacy. Found Trends Theor Comput Sci 9:211\u2013407. https:\/\/doi.org\/10.1561\/0400000042","journal-title":"Found Trends Theor Comput Sci"},{"key":"2234_CR36","unstructured":"McMahan HB, Ramage D, Talwar K, Zhang L (2018) Learning differencially private recurrent language models. International Conference on Learning Representations"},{"key":"2234_CR37","doi-asserted-by":"publisher","first-page":"670","DOI":"10.3390\/electronics11040670","volume":"11","author":"M Shaheen","year":"2022","unstructured":"Shaheen M, Farooq MS, Umer T, Kim B-S (2022) Applications of federated learning; taxonomy, challenges, and research trends. Electronics 11:670","journal-title":"Electronics"},{"key":"2234_CR38","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.inffus.2022.09.011","volume":"90","author":"N Rodr\u00edguez-Barroso","year":"2023","unstructured":"Rodr\u00edguez-Barroso N, Jim\u00e9nez-L\u00f3pez D, Luz\u00f3n MV, Herrera F, Mart\u00ednez-C\u00e1mara E (2023) Survey on federated learning threats: concepts, taxonomy on attacks and defences, experimental study and challenges. Inf Fusion 90:148\u2013173","journal-title":"Inf Fusion"},{"key":"2234_CR39","doi-asserted-by":"publisher","unstructured":"Cummings R, Gupta V, Kimpara D, Morgenstern JH (2019) On the compatibility of privacy and fairness. Adjunct publication of the 27th conference on user modeling, adaptation and personalization, 309\u2013315 https:\/\/doi.org\/10.1145\/3314183.3323847","DOI":"10.1145\/3314183.3323847"},{"key":"2234_CR40","unstructured":"Kusner MJ, Loftus JR, Russell C, Silva R (2017) Counterfactual fairness. 31st conference on neural iInformation processing systems 30, 4069\u20134079"},{"key":"2234_CR41","doi-asserted-by":"crossref","unstructured":"Ding J, Tramel E, Sahu AK, Wu S, Avestimehr S, Zhang T (2022) Federated learning challenges and opportunities: an outlook. ICASSP 2022 - 2022 IEEE iInternational conference on acoustics, speech and signal processing (ICASSP)","DOI":"10.1109\/ICASSP43922.2022.9746925"},{"key":"2234_CR42","first-page":"1","volume":"81","author":"J Buolamwini","year":"2018","unstructured":"Buolamwini J, Gebru T (2018) Gender shades: intersectional accuracy disparities in commercial gender classification. Proc Mach Learn Res 81:1\u201315","journal-title":"Proc Mach Learn Res"},{"key":"2234_CR43","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1145\/3595185","volume":"14","author":"X Zhang","year":"2023","unstructured":"Zhang X, Kang Y, Chen K, Fan L, Yang Q (2023) Trading off privacy, utility, and efficiency in federated learning. ACM Trans Intell Syst Technol 14:98\u201318931. https:\/\/doi.org\/10.1145\/3595185","journal-title":"ACM Trans Intell Syst Technol"},{"key":"2234_CR44","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.procs.2022.12.227","volume":"217","author":"M Khan","year":"2023","unstructured":"Khan M, Glavin FG, Nickles M (2023) Federated learning as a privacy solution - an overview. Procedia Comput Sci 217:316\u2013325. https:\/\/doi.org\/10.1016\/j.procs.2022.12.227","journal-title":"Procedia Comput Sci"},{"key":"2234_CR45","unstructured":"Webster J, Watson RT (2002) Analyzing the past to prepare for the future: writing a literature review. MIS Q 26(2),"},{"key":"2234_CR46","unstructured":"He C, Li S, So J, Zhang M, Wang H, Wang X, Vepakomma P, Singh A, Qiu H, Shen L, Zhao P, Kang Y, Liu Y, Raskar R, Yang Q, Annavaram M, Avestimehr S (2020) Fedml: a research library and benchmark for federated machine learning arXiv:2007.13518"},{"key":"2234_CR47","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.inffus.2020.07.009","volume":"64","author":"NR Barroso","year":"2020","unstructured":"Barroso NR, Stipcich G, Jimenez-Lopez D, Ruiz-Millan JA, Martinez-Camara E, Gonzalez-Seco G, Luzon MV, Veganzones MA, Herrera F (2020) Federated learning and differential privacy: software tools analysis, the sherpa.ai fl framework and methodological guidelines for preserving data privacy. Inf Fusion 64:270\u2013292","journal-title":"Inf Fusion"},{"key":"2234_CR48","unstructured":"Ludwig H, Baracaldo N, Thomas G, Zhou Y, Anwar A, Rajamoni S, Ong YJ, Radhakrishnan J, Verma A, Sinn M, Purcell M, Rawat A, Minh TN, Holohan N, Chakraborty S, Witherspoon S, Steuer D, Wynter L, Hassan H, Laguna S, Yurochkin M, Agarwal M, Chuba E, Abay A (2020) Ibm federated learning: an enterprise framework white paper v0.1 arXiv:2007.10987"},{"key":"2234_CR49","unstructured":"Reina GA, Gruzdev A, Foley P, Perepelkina O, Sharma M, Davidyuk I, Trushkin I, Radionov M, Mokrov A, Agapov D, Martin J, Edwards B, Sheller MJ, Pati S, Moorthy PN, Wang HS, Shah P, Bakas S (2021) Openfl: an open-source framework for federated learning arXiv:2105.06413"},{"key":"2234_CR50","first-page":"1","volume":"22","author":"Y Liu","year":"2021","unstructured":"Liu Y, Fan T, Qian Xu TC, Yang Q (2021) Fate: an industrial grade platform for collaborative learning with data protection. J Mach Learn Res 22:1\u20136","journal-title":"J Mach Learn Res"},{"key":"2234_CR51","unstructured":"Beutel DJ, Topal T, Mathur A, Qiu X, Parcollet T, Lane ND (2020) Flower: a friendly federated learning research framework arXiv:2007.14390"},{"key":"2234_CR52","unstructured":"Dimitriadis D, Garcia MH, Diaz DM, Manoel A, Sim R (2022) Flute: a scalable, extensible framework for high-performance federated learning simulations arXiv:2203.13789"},{"key":"2234_CR53","doi-asserted-by":"crossref","unstructured":"Xie Y, Wang Z, Gao D, Chen D, Yao L, Kuang W, Li Y, Ding B, Zhou J (2023) Federatedscope: a flexible federated learning platform for heterogeneity. Proc VLDB Endowment 16: 1000\u20131012. https:\/\/doi.org\/10.14778\/3579075.3579076","DOI":"10.14778\/3579075.3579081"},{"key":"2234_CR54","first-page":"1","volume":"24","author":"D Zeng","year":"2023","unstructured":"Zeng D, Liang S, Hu X, Wang H, Xu Z (2023) Fedlab: a flexible federated learning framework. J Mach Learn Res 24:1\u20137","journal-title":"J Mach Learn Res"},{"key":"2234_CR55","doi-asserted-by":"publisher","first-page":"13740","DOI":"10.1109\/JIOT.2022.3143842","volume":"9","author":"W Zhuang","year":"2022","unstructured":"Zhuang W, Gan X, Wen Y, Zhang S (2022) Easyfl: a low-code federated learning platform for dummies. IEEE Internet Things J 9:13740\u201313754. https:\/\/doi.org\/10.1109\/JIOT.2022.3143842","journal-title":"IEEE Internet Things J"},{"key":"2234_CR56","unstructured":"FedAI: what is FATE? https:\/\/fate.fedai.org\/overview\/ Accessed 20 Feb 2024"},{"key":"2234_CR57","unstructured":"PaddlePaddle: GitHub Repository PaddlePaddle\/PaddleFL. https:\/\/github.com\/PaddlePaddle\/PaddleFL Accessed 20 Feb 2024"},{"key":"2234_CR58","unstructured":"NVIDIA: NVIDIA Clara: an application framework optimized for healthcare and life sciences developers. https:\/\/developer.nvidia.com\/clara Accessed 30 May 2023"},{"key":"2234_CR59","unstructured":"IBM Research: IBM Federated Learning. https:\/\/ibmfl.res.ibm.com Accessed 20 Feb 2024"},{"key":"2234_CR60","unstructured":"ByteDance: GitHub Repository FedLearner. https:\/\/github.com\/bytedance\/fedlearner Accessed 20 Feb 2024"},{"key":"2234_CR61","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1007\/s10115-022-01664-x","volume":"64","author":"J Liu","year":"2022","unstructured":"Liu J, Huang J, Zhou Y, Li X, Ji S, Xiong H, Dou D (2022) From distributed machine learning to federated learning: a survey. Knowl Inf Syst 64:885\u2013917","journal-title":"Knowl Inf Syst"},{"key":"2234_CR62","doi-asserted-by":"publisher","first-page":"167","DOI":"10.3390\/s21010167","volume":"21","author":"I Kholod","year":"2021","unstructured":"Kholod I, Yanaki E, Fomichev D, Shalugin ED, Novikova E, Filippov E, Nordlund M (2021) Open-source federated learning frameworks for iot: a comparative review and analysis. Sensors 21:167\u2013189. https:\/\/doi.org\/10.3390\/s21010167","journal-title":"Sensors"},{"key":"2234_CR63","unstructured":"TensorFlow: TensorFlow Federated: Machine Learning on Decentralized Data. https:\/\/www.tensorflow.org\/federated Accessed 20 Feb 2024"},{"key":"2234_CR64","unstructured":"OpenMined: OpenMined. https:\/\/www.openmined.org Accessed 20 Feb 2024"},{"key":"2234_CR65","unstructured":"Sherpa.ai: Sherpa.ai: Privacy-Preserving Artificial Intelligence. https:\/\/www.sherpa.ai Accessed 20 Feb 2024"},{"key":"2234_CR66","unstructured":"Liu X, Shi T, Xie C, Li Q, Hu K, Kim H, Xu X, Li B, Song D (2022) Unifed: a benchmark for federated learning frameworks arXiv:2207.10308"},{"key":"2234_CR67","unstructured":"SciKitLearn: Latent Dirichlet Allocation. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.decomposition.LatentDirichletAllocation.html Accessed 24 April 2023"},{"key":"2234_CR68","unstructured":"OpenAI: OpenAI: Pricing. https:\/\/openai.com\/pricing Accessed 20 Feb 2024"},{"key":"2234_CR69","unstructured":"Microsoft: FLUTE: a scalable federated learning simulation platform. https:\/\/bit.ly\/3KnvugJ Accessed 20 Feb 2024"},{"key":"2234_CR70","unstructured":"Caldas S, Duddu SMK, Wu P, Li T, Kone\u010dn\u00fd J, McMahan HB, Smith V, Talwalkar A (2018) Leaf: a benchmark for federated settings"},{"key":"2234_CR71","doi-asserted-by":"crossref","unstructured":"Lai F, Dai Y, Singapuram S, Liu J, Zhu X, Madhyastha H, Chowdhury M Fedscale: Benchmarking model and system performance of federated learning at scale. Proceedings of the 39th international conference on machine learning 162 (2022)","DOI":"10.1145\/3477114.3488760"},{"key":"2234_CR72","unstructured":"FederalLab: GitHub Repository OpenFed. https:\/\/github.com\/FederalLab\/OpenFed Accessed 20 Feb 2024"},{"key":"2234_CR73","unstructured":"OpenMined: GitHub Repository OpenMined\/PySyft. https:\/\/github.com\/OpenMined Accessed 20 Feb 2024"},{"key":"2234_CR74","unstructured":"FedAI: GitHub Repository FedAI\/FATE. https:\/\/github.com\/FederatedAI\/FATE Accessed 20 Feb 2024"},{"key":"2234_CR75","unstructured":"FedML: FedML: The Federated Learning\/Analytics and Edge AI Platform. https:\/\/fedml.ai Accessed 20 Feb 2024"},{"key":"2234_CR76","unstructured":"FedML: GitHub Repository FedML-AI. https:\/\/github.com\/FedML-AI Accessed 20 Feb 2024"},{"key":"2234_CR77","unstructured":"Adap: Adap: Fleet AI. https:\/\/adap.com\/en Accessed 20 Feb 2024"},{"key":"2234_CR78","unstructured":"Adap: GitHub Repository Adap\/Flower. https:\/\/github.com\/adap\/flower Accessed 20 Feb 2024"},{"key":"2234_CR79","unstructured":"TensorFlow: GitHub Repository TensorFlow\/Federated. https:\/\/github.com\/tensorflow\/federated Accessed 20 Feb 2024"},{"key":"2234_CR80","unstructured":"Baidu research: Baidu PaddlePaddle releases 21 new capabilities to accelerate industry-grade model development. http:\/\/research.baidu.com\/Blog\/index-view?id=126 Accessed 07 Aug 2023"},{"key":"2234_CR81","unstructured":"Intel: GitHub Repository Intel\/OpenFL. https:\/\/github.com\/intel\/openfl Accessed 20 Feb 2024"},{"key":"2234_CR82","unstructured":"University of Pennsylvania: CBICA: The Federated Tumor Segmentation (FeTS) Initiative. https:\/\/www.med.upenn.edu\/cbica\/fets\/ Accessed 24 Aug 2022"},{"key":"2234_CR83","unstructured":"IBM: GitHub Repository IBM Federated Learning. https:\/\/github.com\/IBM\/federated-learning-lib Accessed 20 Feb 2024"},{"key":"2234_CR84","unstructured":"NVIDIA: GitHub Repository NVIDIA FLARE. https:\/\/github.com\/NVIDIA\/NVFlare Accessed 20 Feb 2024"},{"key":"2234_CR85","unstructured":"Dogra, P.: Federated learning with FLARE: NVIDIA brings collaborative AI to healthcare and beyond. https:\/\/blogs.nvidia.com\/blog\/2021\/11\/29\/federated-learning-ai-nvidia-flare\/ Accessed 02 Aug 2023"},{"key":"2234_CR86","unstructured":"NVIDIA: NVIDIA FLARE Documentation. https:\/\/nvflare.readthedocs.io\/en\/2.1.1\/index.html Accessed 20 Feb 2024"},{"key":"2234_CR87","unstructured":"Meta Research: GitHub Repository FLSim. https:\/\/github.com\/facebookresearch\/FLSim Accessed 20 Feb 2024"},{"key":"2234_CR88","unstructured":"Microsoft: GitHub Repository Microsoft FLUTE. https:\/\/github.com\/microsoft\/msrflute Accessed 20 Feb 2024"},{"key":"2234_CR89","unstructured":"FederatedScope: FederatedScope. https:\/\/federatedscope.io Accessed 20 Feb 2024"},{"key":"2234_CR90","unstructured":"FederatedScope: GitHub FederatedScope. https:\/\/github.com\/alibaba\/FederatedScope Accessed 20 Feb 2024"},{"key":"2234_CR91","unstructured":"FedLab: GitHub FedLab. https:\/\/github.com\/SMILELab-FL\/FedLab Accessed 20 Feb 2024"},{"key":"2234_CR92","unstructured":"FedLab: ReadTheDocs FedLab. https:\/\/fedlab.readthedocs.io\/en\/master\/ Accessed 20 Feb 2024"},{"key":"2234_CR93","unstructured":"EasyFL: GitHub EasyFL. https:\/\/github.com\/EasyFL-AI\/EasyFL\/tree\/master Accessed 20 Feb 2024"},{"key":"2234_CR94","unstructured":"EasyFL: ReadTheDocs EasyFL. https:\/\/easyfl.readthedocs.io\/en\/latest\/ Accessed 20 Feb 2024"},{"key":"2234_CR95","first-page":"374","volume":"1","author":"K Bonawitz","year":"2019","unstructured":"Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Koneny J, Mazzocchi S, McMahan B, Overveldt TV, Petrou D, Ramage D, Roselander J (2019) Towards federated learning at scale: system design. Proc Mach Learn Syst 1:374\u2013388","journal-title":"Proc Mach Learn Syst"},{"key":"2234_CR96","unstructured":"Mansour Y, Mohri M, Ro J, Suresh AT (2020) Three approaches for personalization with applications to federated learning arXiv:2002.10619"},{"key":"2234_CR97","first-page":"1","volume":"13","author":"PR Silva","year":"2023","unstructured":"Silva PR, Vinagre J, Gama J (2023) Towards federated learning: an overview of methods and applications. WIREs Data Min Knowl Discov 13:1\u201323","journal-title":"WIREs Data Min Knowl Discov"},{"key":"2234_CR98","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2021.07.098","volume":"465","author":"H Zhu","year":"2021","unstructured":"Zhu H, Xu J, Liu S, Jin Y (2021) Federated learning on non-iid data: a survey. Neurocomputing 465:371\u2013390. https:\/\/doi.org\/10.1016\/j.neucom.2021.07.098","journal-title":"Neurocomputing"},{"key":"2234_CR99","doi-asserted-by":"publisher","unstructured":"Nilsson A, Smith S, Ulm G, Gustavsson E, Jirstrand M (2018) A performance evaluation of federated learning algorithms. DIDL \u201918: Proceedings of the second workshop on distributed infrastructures for deep learning 2, 1\u20138 . https:\/\/doi.org\/10.1145\/3286490.3286559","DOI":"10.1145\/3286490.3286559"},{"key":"2234_CR100","doi-asserted-by":"publisher","unstructured":"Asad M, Moustafa A, Ito T, Aslam M (2020) Evaluating the communication efficiency in federated learning algorithms. Proceedings of the 27th ACM symposium on operating systems principles. https:\/\/doi.org\/10.1109\/CSCWD49262.2021.9437738","DOI":"10.1109\/CSCWD49262.2021.9437738"},{"key":"2234_CR101","unstructured":"Smith V, Chiang C-K, Sanjabi M, Talwalkar A (2017) Federated multi-task learning. 31st conference on neural information processing systems (NIPS 2017), 4427\u20134437"},{"issue":"5","key":"2234_CR102","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450288","volume":"54","author":"SK Lo","year":"2021","unstructured":"Lo SK, Lu Q, Wang C, Paik H, Zhu L (2021) A systematic literature review on federated machine learning: from a software engineering perspective. ACM Comput Surv 54(5):1\u201339. https:\/\/doi.org\/10.1145\/3450288","journal-title":"ACM Comput Surv"},{"key":"2234_CR103","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-63076-8_1","volume":"12500","author":"L Lyu","year":"2020","unstructured":"Lyu L, Yu H, Zhao J, Yang Q (2020) Threats to federated learning. Lecture Notes Artif Intell 12500:3\u201316. https:\/\/doi.org\/10.1007\/978-3-030-63076-8_1","journal-title":"Lecture Notes Artif Intell"},{"key":"2234_CR104","unstructured":"Bagdasaryan E, Veit A, Hua Y, Estrin D, Shmatikov V (2020) How to backdoor federated learning. Proceedings of the 23rd international conference on artificial intelligence and statistics, 2938\u20132948"},{"key":"2234_CR105","doi-asserted-by":"crossref","unstructured":"Shejwalkar V, Houmansadr A, Kairouz P, Ramage D (2022) Back to the drawing board: a critical evaluation of poisoning attacks on production federated learning. 2022 IEEE symposium on security and privacy (SP)","DOI":"10.1109\/SP46214.2022.9833647"},{"key":"2234_CR106","unstructured":"Fu J, Zhang X, Ji S, Chen J, Wu J, Guo S, Zhou J, Liu AX, Wang T (2022) Label inference attacks against vertical federated learning. Proceedings of the 31st USENIX security symposium 31"},{"key":"2234_CR107","unstructured":"Feng S, Yu H (2020) Multi-participant multi-class vertical federated learning arXiv:2001.11154"},{"issue":"4","key":"2234_CR108","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/MIS.2020.2988525","volume":"35","author":"Y Liu","year":"2020","unstructured":"Liu Y, Kang Y, Xing C, Chen T, Yang Q (2020) A secure federated transfer learning framework. IEEE Intell Syst 35(4):70\u201382. https:\/\/doi.org\/10.1109\/MIS.2020.2988525","journal-title":"IEEE Intell Syst"},{"key":"2234_CR109","unstructured":"Docker Inc.: The industry-leading container runtime. https:\/\/www.docker.com\/products\/container-runtime\/ Accessed 07 June 2023"},{"issue":"10","key":"2234_CR110","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1145\/262793.262798","volume":"40","author":"M Fayad","year":"1997","unstructured":"Fayad M, Schmidt D (1997) Object-oriented application frameworks. Commun ACM 40(10):32\u201338. https:\/\/doi.org\/10.1145\/262793.262798","journal-title":"Commun ACM"},{"key":"2234_CR111","doi-asserted-by":"publisher","unstructured":"Ge D-Y, Yao X-F, Xiang W-J, Wen, X-J, Liu, E-C (2019) Design of high accuracy detector for mnist handwritten digit recognition based on convolutional neural network. 2019 12th international conference on intelligent computation technology and automation (ICICTA), 658\u2013662 . https:\/\/doi.org\/10.1109\/ICICTA49267.2019.00145","DOI":"10.1109\/ICICTA49267.2019.00145"},{"issue":"6","key":"2234_CR112","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng L (2012) The mnist database of handwritten digit images for machine learning research. IEEE Signals Process Mag 29(6):141\u2013142. https:\/\/doi.org\/10.1109\/MSP.2012.2211477","journal-title":"IEEE Signals Process Mag"},{"key":"2234_CR113","doi-asserted-by":"crossref","unstructured":"Avent B, Korolova A, Zeber D, Hovden T, Livshits B (2017) Blender enabling local search with a hybrid differential privacy model. J Privacy Confid 9, 747\u2013764. DOIurlhttps:\/\/doi.org\/10.29012\/jpc.680","DOI":"10.29012\/jpc.680"},{"key":"2234_CR114","doi-asserted-by":"publisher","unstructured":"Cheu A, Smith AD, Ullman J, Zeber D, Zhilyaev M (2019) Distributed differential privacy via shuffling. IACR Cryptol. ePrint Arch, 375\u2013403 . https:\/\/doi.org\/10.1007\/978-3-030-17653-2_13","DOI":"10.1007\/978-3-030-17653-2_13"},{"key":"2234_CR115","doi-asserted-by":"publisher","unstructured":"Roth E, Noble D, Falk BH, Haeberlen A (2019) Honeycrisp: large-scale differentially private aggregation without a trusted core. Proceedings of the 27th ACM Symposium on Operating Systems Principles, 196\u2013210. https:\/\/doi.org\/10.1145\/3341301.3359660","DOI":"10.1145\/3341301.3359660"},{"key":"2234_CR116","doi-asserted-by":"publisher","unstructured":"Song S, Chaudhuri K, Sarwate AD (2013) Stochastic gradient descent with differentially private updates. 2013 IEEE global conference on signal and information processing, 245\u2013248. https:\/\/doi.org\/10.1109\/GlobalSIP.2013.6736861","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"2234_CR117","unstructured":"Masters O, Hunt H, Steffinlongo E, Crawford J, Bergamaschi F (2019) Towards a homomorphic machine learning big data pipeline for the financial services sector. IACR Cryptol. ePrint Arch, 1\u201321"},{"key":"2234_CR118","doi-asserted-by":"crossref","unstructured":"Yao AC-C (1986) How to generate and exchange secrets. Proceedings of the 27th annual symposium on foundations of computer science, 162\u2013167","DOI":"10.1109\/SFCS.1986.25"},{"issue":"6","key":"2234_CR119","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1038\/s42256-021-00337-8","volume":"3","author":"G Kaissis","year":"2021","unstructured":"Kaissis G, Ziller A, Passerat-Palmbach J, Ryffel T, Usynin D, Trask A, Lima I, Mancuso J, Jungmann F, Steinborn M-M, Saleh A, Makowski M, Rueckert D, Braren R (2021) End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat Mach Intell 3(6):473\u2013484. https:\/\/doi.org\/10.1038\/s42256-021-00337-8","journal-title":"Nat Mach Intell"},{"key":"2234_CR120","doi-asserted-by":"publisher","unstructured":"Subramanyan P, Sinha R, Lebedev IA, Devadas S, Seshia SA (2017) A formal foundation for secure remote execution of enclaves. Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, 2435\u20132450. https:\/\/doi.org\/10.1145\/3133956.3134098","DOI":"10.1145\/3133956.3134098"},{"key":"2234_CR121","unstructured":"Hardy S, Henecka W, Ivey-Law H, Nock R, Patrini G, Smith G, Thorne B (2017) Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption arXiv:1711.10677"},{"key":"2234_CR122","doi-asserted-by":"publisher","unstructured":"Nikolaenko V, Weinsberg U, Ioannidis S, Joye M, Boneh D, Taft N (2013) Privacy-preserving ridge regression on hundreds of millions of records. 2013 IEEE symposium on security and privacy, 334\u2013348. https:\/\/doi.org\/10.1109\/SP.2013.30","DOI":"10.1109\/SP.2013.30"},{"key":"2234_CR123","first-page":"694","volume":"4","author":"J So","year":"2022","unstructured":"So J, He C, Yang C-S, Li S, Yu Q, Ali RE, Guler B, Avestimehr S (2022) Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning. Proc Mach Learn Syst 4:694\u2013720","journal-title":"Proc Mach Learn Syst"},{"key":"2234_CR124","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V (2020) Federated optimization in heterogeneous networks. Proc Mach Learn Syst 2:429\u2013450","journal-title":"Proc Mach Learn Syst"},{"key":"2234_CR125","unstructured":"Reddi SJ, Charles Z, Zaheer M, Garrett Z, Rush K, Kone\u010dn\u00fd J, Kumar S, McMahan HB (2021) Adaptive federated optimization. International conference on learning representations ICLR 2021"},{"key":"2234_CR126","unstructured":"Romanini D, Hall AJ, Papadopoulos P, Titcombe T, Ismail A, Cebere T, Sandmann R, Roehm R, Hoeh MA (2021) Pyvertical: a vertical federated learning framework for multi-headed splitnn. ICLR 2021 Workshop on distributed and private machine learning"},{"key":"2234_CR127","unstructured":"Fan T, Kang Y, Ma G, Chen W, Wei W, Fan L, Yang Q (2023) Fate-llm: a industrial grade federated learning framework for large language models. Arxiv Preprint"},{"key":"2234_CR128","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1016\/j.procs.2022.11.319","volume":"214","author":"A Velez-Esteveza","year":"2022","unstructured":"Velez-Esteveza A, Ducangeb P, Perezc IJ, Coboc MJ (2022) Conceptual structure of federated learning research field. Procedia Comput Sci 214:1374\u20131381","journal-title":"Procedia Comput Sci"},{"key":"2234_CR129","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-70604-3_1","volume":"965","author":"A Farooq","year":"2021","unstructured":"Farooq A, Feizollah A, Rehman MH (2021) Federated learning research trends and bibliometric analysis. Stud Comput Intell 965:1\u201319. https:\/\/doi.org\/10.1007\/978-3-030-70604-3_1","journal-title":"Stud Comput Intell"},{"key":"2234_CR130","doi-asserted-by":"publisher","first-page":"1826","DOI":"10.1109\/TIFS.2023.3340994","volume":"19","author":"M Gong","year":"2024","unstructured":"Gong M, Zhang Y, Gao Y, Qin AK, Wu Y, Wang S, Zhang Y (2024) A multi-modal vertical federated learning framework based on homomorphic encryption. IEEE Trans Inf Forensics Secur 19:1826\u20131839. https:\/\/doi.org\/10.1109\/TIFS.2023.3340994","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2234_CR131","doi-asserted-by":"publisher","unstructured":"Caramalau R, Bhattarai B, Stoyanov D (2023) Federated active learning for target domain generalisation. ArXiv abs\/2312.02247. https:\/\/doi.org\/10.48550\/arXiv.2312.02247","DOI":"10.48550\/arXiv.2312.02247"},{"key":"2234_CR132","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/OJCS.2023.3332351","volume":"5","author":"K Matsuda","year":"2024","unstructured":"Matsuda K, Sasaki Y, Xiao C, Onizuka M (2024) Benchmark for personalized federated learning. IEEE Open J Comput Soc 5:2\u201313. https:\/\/doi.org\/10.1109\/OJCS.2023.3332351","journal-title":"IEEE Open J Comput Soc"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02234-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02234-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02234-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:27:29Z","timestamp":1728451649000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02234-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,28]]},"references-count":132,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["2234"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02234-z","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,28]]},"assertion":[{"value":"13 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2024","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 have no Conflict of interest to declare that are relevant to the content of this article and there are no financial interests","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The data and models used are purely for scientific purposes and do not replace a clinical COVID-19 diagnosis by medical specialists","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}