{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T16:26:31Z","timestamp":1781022391839,"version":"3.54.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3005401"],"award-info":[{"award-number":["2022YFC3005401"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Yunnan Province of China","award":["202203AA080009"],"award-info":[{"award-number":["202203AA080009"]}]},{"name":"Key Research and Development Program of Jiangsu Province of China","award":["BE2020729"],"award-info":[{"award-number":["BE2020729"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10586-024-04810-y","type":"journal-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T19:02:24Z","timestamp":1729969344000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Blockchain federated learning with sparsity for IoMT devices"],"prefix":"10.1007","volume":"28","author":[{"given":"Abdoul Fatakhou","family":"Ba","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mao","family":"Yingchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullahi Uwaisu","family":"Muhammad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omaji","family":"Samuel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tasiu","family":"Muazu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umar Muhammad Mustapha","family":"Kumshe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"key":"4810_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63937-2","volume-title":"Internet of medical things","author":"DJ Hemanth","year":"2021","unstructured":"Hemanth, D.J., Anitha, J., Tsihrintzis, G.A.: Internet of medical things. Springer, Cham (2021)"},{"issue":"1","key":"4810_CR2","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.1038\/s41598-022-07186-4","volume":"12","author":"A Linardos","year":"2022","unstructured":"Linardos, A., Kushibar, K., Walsh, S., et al.: Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease. Sci. Rep. 12(1), 3551 (2022). https:\/\/doi.org\/10.1038\/s41598-022-07186-4","journal-title":"Sci. Rep."},{"issue":"2","key":"4810_CR3","doi-asserted-by":"publisher","first-page":"852","DOI":"10.21037\/qims-20-595","volume":"11","author":"D Ng","year":"2021","unstructured":"Ng, D., Lan, X., Yao, M.M.S., et al.: Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets. Quant. Imaging Med. Surg. 11(2), 852 (2021)","journal-title":"Quant. Imaging Med. Surg."},{"issue":"6","key":"4810_CR4","doi-asserted-by":"publisher","first-page":"4177","DOI":"10.1109\/TII.2019.2942190","volume":"16","author":"Y Lu","year":"2020","unstructured":"Lu, Y., Huang, X., Dai, Y., et al.: Blockchain and federated learning for privacy-preserved data sharing in industrial iot. IEEE Trans. Ind. Inform. 16(6), 4177\u20134186 (2020). https:\/\/doi.org\/10.1109\/TII.2019.2942190","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"6","key":"4810_CR5","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1109\/LCOMM.2019.2921755","volume":"24","author":"H Kim","year":"2020","unstructured":"Kim, H., Park, J., Bennis, M., et al.: Blockchained on-device federated learning. IEEE Commun. Lett. 24(6), 1279\u20131283 (2020). https:\/\/doi.org\/10.1109\/LCOMM.2019.2921755","journal-title":"IEEE Commun. Lett."},{"key":"4810_CR6","unstructured":"Chen, H., Asif, S. A., Park, J., et\u00a0al.: Robust blockchained federated learning with model validation and proof-of-stake inspired consensus (2021). http:\/\/arxiv.org\/abs\/2101.03300"},{"key":"4810_CR7","unstructured":"McMahan, B., Moore, E., Ramage, D., et\u00a0al.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp. 1273\u20131282, PMLR (2017)"},{"key":"4810_CR8","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Zaheer, M., et al.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429\u2013450 (2020)","journal-title":"Proc. Mach. Learn. Syst."},{"key":"4810_CR9","unstructured":"Acar, D.A.E., Zhao, Y., Navarro, R.M., et\u00a0al.: Federated learning based on dynamic regularization (2021). arXiv preprint http:\/\/arxiv.org\/abs\/2111.04263"},{"key":"4810_CR10","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., et\u00a0al.: Scaffold: stochastic controlled averaging for federated learning. In: International conference on machine learning (2019). https:\/\/api.semanticscholar.org\/CorpusID:214069261"},{"key":"4810_CR11","doi-asserted-by":"publisher","unstructured":"Misonne, T., Jodogne, S.: Federated learning for heart segmentation. In: 2022 IEEE 14th image, video, and multidimensional signal processing workshop (IVMSP), pp. 1\u20135 (2022). https:\/\/doi.org\/10.1109\/IVMSP54334.2022.9816345","DOI":"10.1109\/IVMSP54334.2022.9816345"},{"key":"4810_CR12","doi-asserted-by":"crossref","unstructured":"Huang, L., Liu, D.: Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records.(2019). http:\/\/arxiv.org\/abs\/1903.09296","DOI":"10.1016\/j.jbi.2019.103291"},{"key":"4810_CR13","unstructured":"Choudhury, O., Park, Y., Salonidis, T., et al.: Predicting adverse drug reactions on distributed health data using federated learning. In: AMIA annual symposium proceedings AMIA symposium, vol. 2019, pp. 313\u2013322 (2020)"},{"issue":"1","key":"4810_CR14","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke, N., Hancox, J., Li, W., et al.: The future of digital health with federated learning. NPJ Digit. Med. 3(1), 119 (2020). https:\/\/doi.org\/10.1038\/s41746-020-00323-1","journal-title":"NPJ Digit. Med."},{"key":"4810_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04357-y","author":"AH Allam","year":"2024","unstructured":"Allam, A.H., Gomaa, I., Zayed, H.H., et al.: Iot-based ehealth using blockchain technology: a survey. Cluster Comput. (2024). https:\/\/doi.org\/10.1007\/s10586-024-04357-y","journal-title":"Cluster Comput."},{"issue":"2","key":"4810_CR16","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1109\/JBHI.2022.3167256","volume":"27","author":"A Alamleh","year":"2023","unstructured":"Alamleh, A., Albahri, O.S., Zaidan, A.A., et al.: Federated learning for iomt applications: a standardization and benchmarking framework of intrusion detection systems. IEEE J. Biomed. Health Inform. 27(2), 878\u2013887 (2023). https:\/\/doi.org\/10.1109\/JBHI.2022.3167256","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"4810_CR17","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-030-33966-1_10","volume-title":"Intelligent, secure big health data management using deep learning and blockchain technology: an overview","author":"S Saif","year":"2020","unstructured":"Saif, S., Biswas, S., Chattopadhyay, S.: Intelligent, secure big health data management using deep learning and blockchain technology: an overview, pp. 187\u2013209. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-33966-1_10"},{"key":"4810_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04515-2","author":"NA Jalali","year":"2024","unstructured":"Jalali, N.A., Hongsong, C.: Comprehensive framework for implementing blockchain-enabled federated learning and full homomorphic encryption for chatbot security system. Clust. Comput. (2024). https:\/\/doi.org\/10.1007\/s10586-024-04515-2","journal-title":"Clust. Comput."},{"key":"4810_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3213306","author":"W Wang","year":"2022","unstructured":"Wang, W., Yang, Y., Yin, Z., et al.: Bsif: blockchain-based secure, interactive, and fair mobile crowdsensing. IEEE J. Sel. Areas Commun. (2022). https:\/\/doi.org\/10.1109\/JSAC.2022.3213306","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"1","key":"4810_CR20","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/s13721-022-00363-5","volume":"11","author":"JC Priya","year":"2022","unstructured":"Priya, J.C., Choudhury, T., Khanna, A., et al.: Blockchain-based transfer learning for health screening with digital anthropometry from body images. Netw. Model. Anal. Health Inform. Bioinform. 11(1), 23 (2022). https:\/\/doi.org\/10.1007\/s13721-022-00363-5","journal-title":"Netw. Model. Anal. Health Inform. Bioinform."},{"key":"4810_CR21","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1007\/978-3-031-09917-5_31","volume-title":"Web Eng.","author":"P Sorino","year":"2022","unstructured":"Sorino, P.: Blockchain and AI to build an Alzheimer\u2019s risk calculator. In: Di Noia, T., Ko, I.Y., Schedl, M., et al. (eds.) Web Eng., pp. 432\u2013436. Springer, Cham (2022)"},{"key":"4810_CR22","doi-asserted-by":"publisher","unstructured":"Wang, S.: Blockfedml: blockchained federated machine learning systems. In: 2019 international conference on intelligent computing, automation and systems (ICICAS), pp. 751\u2013756 (2019). https:\/\/doi.org\/10.1109\/ICICAS48597.2019.00162","DOI":"10.1109\/ICICAS48597.2019.00162"},{"key":"4810_CR23","doi-asserted-by":"publisher","unstructured":"Majeed, U., Hong, C.S.: Flchain: federated learning via mec-enabled blockchain network. In: 2019 20th Asia-Pacific network operations and management symposium (APNOMS), pp. 1\u20134 (2019). https:\/\/doi.org\/10.23919\/APNOMS.2019.8892848","DOI":"10.23919\/APNOMS.2019.8892848"},{"issue":"1","key":"4810_CR24","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/MNET.115.2200014","volume":"37","author":"Y Wang","year":"2023","unstructured":"Wang, Y., Zhou, J., Feng, G., et al.: Blockchain assisted federated learning for enabling network edge intelligence. IEEE Netw. 37(1), 96\u2013102 (2023). https:\/\/doi.org\/10.1109\/MNET.115.2200014","journal-title":"IEEE Netw."},{"key":"4810_CR25","doi-asserted-by":"publisher","DOI":"10.1145\/3600225","author":"Y Tian","year":"2023","unstructured":"Tian, Y., Guo, Z., Zhang, J., et al.: Dfl: high-performance blockchain-based federated learning. Distrib Ledger Technol (2023). https:\/\/doi.org\/10.1145\/3600225","journal-title":"Distrib Ledger Technol"},{"issue":"4","key":"4810_CR26","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/TSUSC.2023.3279111","volume":"8","author":"Z Lian","year":"2023","unstructured":"Lian, Z., Wang, W., Han, Z., et al.: Blockchain-based personalized federated learning for internet of medical things. IEEE Trans. Sustain. Comput. 8(4), 694\u2013702 (2023). https:\/\/doi.org\/10.1109\/TSUSC.2023.3279111","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"4810_CR27","unstructured":"Kairouz, P., McMahan, H.B., Avent, B., et\u00a0al.: Advances and open problems in federated learning (2021). http:\/\/arxiv.org\/abs\/1912.04977"},{"key":"4810_CR28","unstructured":"Li, D., Wang, J.: Fedmd: heterogenous federated learning via model distillation (2019). arXiv preprint http:\/\/arxiv.org\/abs\/1910.03581"},{"key":"4810_CR29","unstructured":"Zhu, Z., Hong, J., Zhou, J.: Data-free knowledge distillation for heterogeneous federated learning. In: International conference on machine learning, PMLR, pp. 12878\u201312889 (2021)"},{"issue":"7","key":"4810_CR30","doi-asserted-by":"publisher","first-page":"4788","DOI":"10.1109\/TII.2021.3113708","volume":"18","author":"X Xu","year":"2022","unstructured":"Xu, X., Peng, H., Bhuiyan, M.Z.A., et al.: Privacy-preserving federated depression detection from multisource mobile health data. IEEE Trans. Ind. Inform. 18(7), 4788\u20134797 (2022). https:\/\/doi.org\/10.1109\/TII.2021.3113708","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"2","key":"4810_CR31","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/JBHI.2022.3187471","volume":"27","author":"Z Xu","year":"2023","unstructured":"Xu, Z., Guo, Y., Chakraborty, C., et al.: A simple federated learning-based scheme for security enhancement over internet of medical things. IEEE J. Biomed. Health Inform. 27(2), 652\u2013663 (2023). https:\/\/doi.org\/10.1109\/JBHI.2022.3187471","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"4810_CR32","unstructured":"Gupta, M.: Blockchain for Dummies, 3rd IBM Limited Edition. John Wiley & Sons, Inc., America (2020)"},{"issue":"6","key":"4810_CR33","doi-asserted-by":"publisher","first-page":"3620","DOI":"10.1109\/TII.2019.2908497","volume":"15","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Su, Z., Zhang, N.: Bsis: blockchain-based secure incentive scheme for energy delivery in vehicular energy network. IEEE Trans. Ind. Inform. 15(6), 3620\u20133631 (2019). https:\/\/doi.org\/10.1109\/TII.2019.2908497","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"2","key":"4810_CR34","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1109\/JBHI.2022.3143576","volume":"27","author":"O Samuel","year":"2023","unstructured":"Samuel, O., Omojo, A.B., Onuja, A.M., et al.: Iomt: a covid-19 healthcare system driven by federated learning and blockchain. IEEE J. Biomed. Health Inform. 27(2), 823\u2013834 (2023). https:\/\/doi.org\/10.1109\/JBHI.2022.3143576","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"4810_CR35","doi-asserted-by":"crossref","unstructured":"Sneha, G., Songara, D., Saurabh, M.: The winning strategy of tic tac toe game model by using theoretical computer science. In: In 2017 international conference on computer. Communications and Electronics (Comptelix), pp. 89\u201395. IEEE (2017)","DOI":"10.1109\/COMPTELIX.2017.8003944"},{"issue":"2","key":"4810_CR36","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., et al.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1\u201319 (2019)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"5","key":"4810_CR37","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/s00287-019-01205-x","volume":"42","author":"S Truex","year":"2019","unstructured":"Truex, S., Baracaldo, N., Anwar, A., et al.: A hybrid approach to privacy-preserving federated learning. Inform. Spektrum 42(5), 356\u2013357 (2019). https:\/\/doi.org\/10.1007\/s00287-019-01205-x","journal-title":"Inform. Spektrum"},{"key":"4810_CR38","doi-asserted-by":"publisher","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","volume":"15","author":"K Wei","year":"2020","unstructured":"Wei, K., Li, J., Ding, M., et al.: Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans. Inf. Forensics Secur. 15, 3454\u20133469 (2020). https:\/\/doi.org\/10.1109\/TIFS.2020.2988575","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"5","key":"4810_CR39","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1109\/TNSE.2022.3185327","volume":"10","author":"L Zhang","year":"2023","unstructured":"Zhang, L., Xu, J., Vijayakumar, P., et al.: Homomorphic encryption-based privacy-preserving federated learning in iot-enabled healthcare system. IEEE Trans. Netw. Sci. Eng. 10(5), 2864\u20132880 (2023). https:\/\/doi.org\/10.1109\/TNSE.2022.3185327","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"4810_CR40","doi-asserted-by":"publisher","first-page":"161377","DOI":"10.1109\/ACCESS.2020.3021613","volume":"8","author":"O Samuel","year":"2020","unstructured":"Samuel, O., Javaid, N., Khalid, A., et al.: Towards real-time energy management of multi-microgrid using a deep convolution neural network and cooperative game approach. IEEE Access 8, 161377\u2013161395 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3021613","journal-title":"IEEE Access"},{"issue":"8","key":"4810_CR41","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1109\/LWC.2022.3167568","volume":"11","author":"B Omoniwa","year":"2022","unstructured":"Omoniwa, B., Galkin, B., Dusparic, I.: Optimizing energy efficiency in uav-assisted networks using deep reinforcement learning. IEEE Wirel. Commun. Lett. 11(8), 1590\u20131594 (2022). https:\/\/doi.org\/10.1109\/LWC.2022.3167568","journal-title":"IEEE Wirel. Commun. Lett."},{"issue":"7","key":"4810_CR42","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.1007\/s00521-020-05136-7","volume":"33","author":"S Liu","year":"2021","unstructured":"Liu, S., Mocanu, D.C., Matavalam, A.R.R., et al.: Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware. Neural Comput. Appl. 33(7), 2589\u20132604 (2021). https:\/\/doi.org\/10.1007\/s00521-020-05136-7","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"4810_CR43","doi-asserted-by":"publisher","first-page":"2383","DOI":"10.1038\/s41467-018-04316-3","volume":"9","author":"DC Mocanu","year":"2018","unstructured":"Mocanu, D.C., Mocanu, E., Stone, P., et al.: Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nat. Commun. 9(1), 2383 (2018). https:\/\/doi.org\/10.1038\/s41467-018-04316-3","journal-title":"Nat. Commun."},{"issue":"8","key":"4810_CR44","doi-asserted-by":"publisher","first-page":"5289","DOI":"10.1109\/TIT.2023.3265009","volume":"69","author":"J Ding","year":"2023","unstructured":"Ding, J., Du, H.: Detection threshold for correlated erdHos-r\u00e9nyi graphs via densest subgraph. IEEE Trans. Inf. Theory 69(8), 5289\u20135298 (2023)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"4810_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2023.128713","volume":"621","author":"JV Merenda","year":"2023","unstructured":"Merenda, J.V., Bruno, O.M.: Using deterministic self-avoiding walks as a small-world metric on Watts-Strogatz networks. Physica A 621, 128713 (2023)","journal-title":"Physica A"},{"key":"4810_CR46","doi-asserted-by":"crossref","unstructured":"Bhat, S., Sai, V.R., Mundody, S., et\u00a0al.: Leveraging sir and barab\u00e1si-albert models for epidemic modelling. In: 2024 35th conference of open innovations association (FRUCT), pp. 170\u2013178. IEEE (2024)","DOI":"10.23919\/FRUCT61870.2024.10516420"},{"key":"4810_CR47","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2886795","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Zhu, H., Wang, F., et al.: Security and privacy threats to federated learning: issues, methods, and challenges. Secur. Commun. Netw. (2022). https:\/\/doi.org\/10.1155\/2022\/2886795","journal-title":"Secur. Commun. Netw."},{"issue":"4","key":"4810_CR48","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1109\/JIOT.2021.3077803","volume":"9","author":"V Mothukuri","year":"2022","unstructured":"Mothukuri, V., Khare, P., Parizi, R.M., et al.: Federated-learning-based anomaly detection for iot security attacks. IEEE Internet Things J. 9(4), 2545\u20132554 (2022). https:\/\/doi.org\/10.1109\/JIOT.2021.3077803","journal-title":"IEEE Internet Things J."},{"key":"4810_CR49","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1016\/j.future.2020.10.007","volume":"115","author":"V Mothukuri","year":"2021","unstructured":"Mothukuri, V., Parizi, R.M., Pouriyeh, S., et al.: A survey on security and privacy of federated learning. Future Gener. Comput. Syst. 115, 619\u2013640 (2021). https:\/\/doi.org\/10.1016\/j.future.2020.10.007","journal-title":"Future Gener. Comput. Syst."},{"key":"4810_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104468","volume":"106","author":"A Blanco-Justicia","year":"2021","unstructured":"Blanco-Justicia, A., Domingo-Ferrer, J., Mart\u00ednez, S., et al.: Achieving security and privacy in federated learning systems: survey, research challenges and future directions. Eng. Appl. Artif. Intell. 106, 104468 (2021). https:\/\/doi.org\/10.1016\/j.engappai.2021.104468","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"4810_CR51","first-page":"3452","volume":"5","author":"L Pengrui","year":"2022","unstructured":"Pengrui, L., Xu, X., Wang, W.: Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives. Cybersecurity 5(1), 3452\u20133469 (2022)","journal-title":"Cybersecurity"},{"issue":"1","key":"4810_CR52","first-page":"20","volume":"2","author":"JS Malhar","year":"2020","unstructured":"Malhar, J.S., Farnan, T., Koushanfar, F.: A taxonomy of attacks on federated learning. IEEE Secur. Priv. 2(1), 20\u201328 (2020)","journal-title":"IEEE Secur. Priv."},{"issue":"2","key":"4810_CR53","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1109\/TPAMI.2022.3162397","volume":"45","author":"M Goldblum","year":"2022","unstructured":"Goldblum, M., Tsipras, D., Xie, C., et al.: Dataset security for machine learning: data poisoning, backdoor attacks, and defenses. IEEE Trans. Pattern Anal. Mach. Intell. 45(2), 1563\u20131580 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"4810_CR54","doi-asserted-by":"publisher","first-page":"73","DOI":"10.3390\/fi13030073","volume":"13","author":"X Zhou","year":"2021","unstructured":"Zhou, X., Xu, M., Wu, Y., et al.: Deep model poisoning attack on federated learning. Future Internet 13(3), 73 (2021)","journal-title":"Future Internet"},{"issue":"5","key":"4810_CR55","first-page":"2438","volume":"18","author":"J Weng","year":"2019","unstructured":"Weng, J., Weng, J., Zhang, J., et al.: Deepchain: auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Trans. Dependable Secure Comput. 18(5), 2438\u20132455 (2019)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"4810_CR56","doi-asserted-by":"publisher","first-page":"41928","DOI":"10.1109\/ACCESS.2023.3269980","volume":"11","author":"HNC Neto","year":"2023","unstructured":"Neto, H.N.C., Hribar, J., Dusparic, I., et al.: A survey on securing federated learning: analysis of applications, attacks, challenges, and trends. IEEE Access 11, 41928\u201341953 (2023)","journal-title":"IEEE Access"},{"key":"4810_CR57","unstructured":"Fredrikson, M., Lantz, E., Jha, S., et\u00a0al.: Privacy in pharmacogenetics: an $$\\{$$End-to-End$$\\}$$ case study of personalized warfarin dosing. In: 23rd USENIX security symposium (USENIX Security 14), pp. 17\u201332 (2014)"},{"issue":"3","key":"4810_CR58","doi-asserted-by":"publisher","first-page":"4719","DOI":"10.1109\/JIOT.2018.2878154","volume":"6","author":"J Pan","year":"2019","unstructured":"Pan, J., Wang, J., Hester, A., et al.: Edgechain: an edge-iot framework and prototype based on blockchain and smart contracts. IEEE Internet Things J. 6(3), 4719\u20134732 (2019). https:\/\/doi.org\/10.1109\/JIOT.2018.2878154","journal-title":"IEEE Internet Things J."},{"issue":"14","key":"4810_CR59","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., et al.: Blockchain-federated-learning and deep learning models for covid-19 detection using ct imaging. IEEE Sens. J. 21(14), 16301\u201316314 (2021). https:\/\/doi.org\/10.1109\/jsen.2021.3076767","journal-title":"IEEE Sens. J."},{"issue":"7","key":"4810_CR60","doi-asserted-by":"publisher","first-page":"5926","DOI":"10.1109\/JIOT.2020.3032544","volume":"8","author":"W Zhang","year":"2021","unstructured":"Zhang, W., Lu, Q., Yu, Q., et al.: Blockchain-based federated learning for device failure detection in industrial iot. IEEE Internet Things J. 8(7), 5926\u20135937 (2021). https:\/\/doi.org\/10.1109\/JIOT.2020.3032544","journal-title":"IEEE Internet Things J."},{"issue":"1","key":"4810_CR61","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41597-022-01721-8","volume":"10","author":"J Yang","year":"2023","unstructured":"Yang, J., Shi, R., Wei, D., et al.: Medmnist v2\u2014a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Sci. Data 10(1), 41 (2023). https:\/\/doi.org\/10.1038\/s41597-022-01721-8","journal-title":"Sci. Data"},{"key":"4810_CR62","unstructured":"Anguita, D., Ghio, A., Oneto, L., et\u00a0al.: A public domain dataset for human activity recognition using smartphones. In: The European symposium on artificial neural networks (2013). https:\/\/api.semanticscholar.org\/CorpusID:6975432"},{"key":"4810_CR63","doi-asserted-by":"publisher","unstructured":"Reiss, A., Stricker, D.: Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th international symposium on wearable computers, pp. 108\u2013109 (2012). https:\/\/doi.org\/10.1109\/ISWC.2012.13","DOI":"10.1109\/ISWC.2012.13"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04810-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-024-04810-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-024-04810-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T15:14:34Z","timestamp":1736522074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-024-04810-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,26]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["4810"],"URL":"https:\/\/doi.org\/10.1007\/s10586-024-04810-y","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,26]]},"assertion":[{"value":"22 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2024","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":"47"}}