{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T04:26:06Z","timestamp":1758169566955,"version":"3.44.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T00:00:00Z","timestamp":1756512000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T00:00:00Z","timestamp":1756512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Vietnam National University HoChiMinh City","award":["DS2024-26-04"],"award-info":[{"award-number":["DS2024-26-04"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s10586-025-05247-7","type":"journal-article","created":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T10:44:09Z","timestamp":1756550649000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PoFQ: a blockchain consensus protocol for decentralized federated learning-based threat hunting approach in a trustless computing landscape"],"prefix":"10.1007","volume":"28","author":[{"given":"Vo Truong Trung","family":"Hieu","sequence":"first","affiliation":[]},{"given":"Nguyen Huu","family":"Quyen","sequence":"additional","affiliation":[]},{"given":"Hien","family":"Do Hoang","sequence":"additional","affiliation":[]},{"given":"Phan The","family":"Duy","sequence":"additional","affiliation":[]},{"given":"Van-Hau","family":"Pham","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,30]]},"reference":[{"key":"5247_CR1","doi-asserted-by":"publisher","DOI":"10.1145\/3372823","author":"N Kaloudi","year":"2020","unstructured":"Kaloudi, N., Li, J.: The AI-based cyber threat landscape: a survey. ACM Comput. Surv. (2020). https:\/\/doi.org\/10.1145\/3372823","journal-title":"ACM Comput. Surv."},{"key":"5247_CR2","doi-asserted-by":"publisher","first-page":"227756","DOI":"10.1109\/ACCESS.2020.3045514","volume":"8","author":"M Vielberth","year":"2020","unstructured":"Vielberth, M., B\u00f6hm, F., Fichtinger, I., Pernul, G.: Security operations center: a systematic study and open challenges. IEEE Access 8, 227756\u2013227779 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3045514","journal-title":"IEEE Access"},{"issue":"3","key":"5247_CR3","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1080\/23742917.2019.1698178","volume":"4","author":"PR Enoch Agyepong Yulia Cherdantseva","year":"2020","unstructured":"Enoch Agyepong Yulia Cherdantseva, P.R., Burnap, P.: Challenges and performance metrics for security operations center analysts: a systematic review. J. Cyber Secur. Technol. 4(3), 125\u2013152 (2020). https:\/\/doi.org\/10.1080\/23742917.2019.1698178","journal-title":"J. Cyber Secur. Technol."},{"issue":"4","key":"5247_CR4","doi-asserted-by":"publisher","first-page":"2299","DOI":"10.1109\/COMST.2023.3299519","volume":"25","author":"B Nour","year":"2023","unstructured":"Nour, B., Pourzandi, M., Debbabi, M.: A survey on threat hunting in enterprise networks. IEEE Commun. Surv. Tutor. 25(4), 2299\u20132324 (2023). https:\/\/doi.org\/10.1109\/COMST.2023.3299519","journal-title":"IEEE Commun. Surv. Tutor."},{"doi-asserted-by":"publisher","unstructured":"Gao, P., Shao, F., Liu, X., Xiao, X., Qin, Z., Xu, F., Mittal, P., Kulkarni, S.R., Song, D.: Enabling efficient cyber threat hunting with cyber threat intelligence. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 193\u2013204 (2021). https:\/\/doi.org\/10.1109\/ICDE51399.2021.00024","key":"5247_CR5","DOI":"10.1109\/ICDE51399.2021.00024"},{"doi-asserted-by":"publisher","unstructured":"Khramtsova, E., Hammerschmidt, C., Lagraa, S., State, R.: Federated learning for cyber security: Soc collaboration for malicious url detection. In: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS), pp. 1316\u20131321 (2020). https:\/\/doi.org\/10.1109\/ICDCS47774.2020.00171","key":"5247_CR6","DOI":"10.1109\/ICDCS47774.2020.00171"},{"key":"5247_CR7","doi-asserted-by":"publisher","first-page":"140699","DOI":"10.1109\/ACCESS.2020.3013541","volume":"8","author":"M Aledhari","year":"2020","unstructured":"Aledhari, M., Razzak, R., Parizi, R.M., Saeed, F.: Federated learning: a survey on enabling technologies, protocols, and applications. IEEE Access 8, 140699\u2013140725 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3013541","journal-title":"IEEE Access"},{"key":"5247_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775","volume":"216","author":"C Zhang","year":"2021","unstructured":"Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., Gao, Y.: A survey on federated learning. Knowl.-Based Syst. 216, 106775 (2021). https:\/\/doi.org\/10.1016\/j.knosys.2021.106775","journal-title":"Knowl.-Based Syst."},{"key":"5247_CR9","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12020260","author":"HS Sikandar","year":"2023","unstructured":"Sikandar, H.S., Waheed, H., Tahir, S., Malik, S.U.R., Rafique, W.: A detailed survey on federated learning attacks and defenses. Electronics (2023). https:\/\/doi.org\/10.3390\/electronics12020260","journal-title":"Electronics"},{"key":"5247_CR10","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., Yu, P.S.: Privacy and robustness in federated learning: attacks and defenses. IEEE Trans. Neural Netw. Learn. Syst. (2022). https:\/\/doi.org\/10.1109\/TNNLS.2022.3216981","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"5247_CR11","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s42400-021-00105-6","volume":"5","author":"P Liu","year":"2022","unstructured":"Liu, P., Xu, X., Wang, W.: Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives. Cybersecurity 5(1), 4 (2022). https:\/\/doi.org\/10.1186\/s42400-021-00105-6","journal-title":"Cybersecurity"},{"doi-asserted-by":"crossref","unstructured":"Feng, S., Mohammady, M., Hong, H., Yan, S., Kundu, A., Wang, B., Hong, Y.: Universally harmonizing differential privacy mechanisms for federated learning: boosting accuracy and convergence (2024)","key":"5247_CR12","DOI":"10.1145\/3714393.3726517"},{"key":"5247_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102198","volume":"105","author":"TH Rafi","year":"2024","unstructured":"Rafi, T.H., Noor, F.A., Hussain, T., Chae, D.K.: Fairness and privacy preserving in federated learning: a survey. Inf. Fusion 105, 102198 (2024). https:\/\/doi.org\/10.1016\/j.inffus.2023.102198","journal-title":"Inf. Fusion"},{"key":"5247_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103205","volume":"129","author":"YC Lai","year":"2023","unstructured":"Lai, Y.C., Lin, J.Y., Lin, Y.D., Hwang, R.H., Lin, P.C., Wu, H.K., Chen, C.K.: Two-phase defense against poisoning attacks on federated learning-based intrusion detection. Comput. Secur. 129, 103205 (2023). https:\/\/doi.org\/10.1016\/j.cose.2023.103205","journal-title":"Comput. Secur."},{"key":"5247_CR15","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1109\/TIFS.2023.3249568","volume":"18","author":"Y Jiang","year":"2023","unstructured":"Jiang, Y., Zhang, W., Chen, Y.: Data quality detection mechanism against label flipping attacks in federated learning. IEEE Trans. Inf. Forensics Secur. 18, 1625\u20131637 (2023). https:\/\/doi.org\/10.1109\/TIFS.2023.3249568","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"5247_CR16","volume":"87","author":"TD Luong","year":"2024","unstructured":"Luong, T.D., Tien, V.M., Quyen, N.H., Duy, P.T., Pham, V.H., et al.: Fed-lsae: Thwarting poisoning attacks against federated cyber threat detection system via autoencoder-based latent space inspection. J. Inf. Secur. Appl. 87, 103916 (2024)","journal-title":"J. Inf. Secur. Appl."},{"doi-asserted-by":"crossref","unstructured":"Li, X., Qu, Z., Zhao, S., Tang, B., Lu, Z., Liu, Y.: Lomar: A local defense against poisoning attack on federated learning (2022)","key":"5247_CR17","DOI":"10.1109\/TDSC.2021.3135422"},{"issue":"4","key":"5247_CR18","doi-asserted-by":"publisher","first-page":"2983","DOI":"10.1109\/COMST.2023.3315746","volume":"25","author":"ET Mart\u00ednez Beltr\u00e1n","year":"2023","unstructured":"Mart\u00ednez Beltr\u00e1n, E.T., P\u00e9rez, M.Q., S\u00e1nchez, P.M.S., Bernal, S.L., Bovet, G., P\u00e9rez, M.G., P\u00e9rez, G.M., Celdr\u00e1n, A.H.: Decentralized federated learning: fundamentals, state of the art, frameworks, trends, and challenges. IEEE Commun. Surv. Tutor. 25(4), 2983\u20133013 (2023). https:\/\/doi.org\/10.1109\/COMST.2023.3315746","journal-title":"IEEE Commun. Surv. Tutor."},{"doi-asserted-by":"crossref","unstructured":"Dong, N., Wang, Z., Sun, J., Kampffmeyer, M., Knottenbelt, W., Xing, E.: Defending against poisoning attacks in federated learning with blockchain (2024)","key":"5247_CR19","DOI":"10.1109\/TAI.2024.3376651"},{"issue":"1","key":"5247_CR20","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1109\/tetc.2023.3268186","volume":"12","author":"AP Kalapaaking","year":"2024","unstructured":"Kalapaaking, A.P., Khalil, I., Yi, X.: Blockchain-based federated learning with SMPC model verification against poisoning attack for healthcare systems. IEEE Trans. Emerg. Top. Comput. 12(1), 269\u2013280 (2024). https:\/\/doi.org\/10.1109\/tetc.2023.3268186","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"doi-asserted-by":"crossref","unstructured":"Qin, Z., Yan, X., Zhou, M., Deng, S.: Blockdfl: a blockchain-based fully decentralized peer-to-peer federated learning framework. In: Proceedings of the ACM Web Conference 2024, p. 2914\u20132925. Association for Computing Machinery (2024)","key":"5247_CR21","DOI":"10.1145\/3589334.3645425"},{"unstructured":"Castro, M., Liskov, B.: Practical Byzantine Fault Tolerance. OSDI (1999)","key":"5247_CR22"},{"unstructured":"Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008)","key":"5247_CR23"},{"unstructured":"Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Decentralized Business Review, p. 21260 (2008)","key":"5247_CR24"},{"key":"5247_CR25","doi-asserted-by":"publisher","first-page":"43620","DOI":"10.1109\/ACCESS.2021.3065880","volume":"9","author":"B Lashkari","year":"2021","unstructured":"Lashkari, B., Musilek, P.: A comprehensive review of blockchain consensus mechanisms. IEEE Access 9, 43620\u201343652 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3065880","journal-title":"IEEE Access"},{"key":"5247_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114384","volume":"168","author":"S Bouraga","year":"2021","unstructured":"Bouraga, S.: A taxonomy of blockchain consensus protocols: a survey and classification framework. Expert Syst. Appl. 168, 114384 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2020.114384","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"5247_CR27","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1109\/COMST.2020.2969706","volume":"22","author":"Y Xiao","year":"2020","unstructured":"Xiao, Y., Zhang, N., Lou, W., Hou, Y.T.: A survey of distributed consensus protocols for blockchain networks. IEEE Commun. Surv. Tutor. 22(2), 1432\u20131465 (2020). https:\/\/doi.org\/10.1109\/COMST.2020.2969706","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"2","key":"5247_CR28","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.icte.2019.08.001","volume":"6","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Lee, J.H.: Analysis of the main consensus protocols of blockchain. ICT Express 6(2), 93\u201397 (2020). https:\/\/doi.org\/10.1016\/j.icte.2019.08.001","journal-title":"ICT Express"},{"issue":"6","key":"5247_CR29","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., Maharjan, S., Zhang, Y.: Blockchain and federated learning for privacy-preserved data sharing in industrial iot. IEEE Trans. Industr. Inf. 16(6), 4177\u20134186 (2020). https:\/\/doi.org\/10.1109\/TII.2019.2942190","journal-title":"IEEE Trans. Industr. Inf."},{"doi-asserted-by":"crossref","unstructured":"Sokhankhosh, A., Rouhani, S.: Proof-of-collaborative-learning: a multi-winner federated learning consensus algorithm. In: 2024 IEEE International Conference on Blockchain (Blockchain), pp. 370\u2013377. IEEE (2024)","key":"5247_CR30","DOI":"10.1109\/Blockchain62396.2024.00055"},{"issue":"11","key":"5247_CR31","doi-asserted-by":"publisher","first-page":"7920","DOI":"10.1109\/TII.2022.3167663","volume":"18","author":"M Abdel-Basset","year":"2022","unstructured":"Abdel-Basset, M., Moustafa, N., Hawash, H.: Privacy-preserved cyberattack detection in industrial edge of things (IEOT): a blockchain-orchestrated federated learning approach. IEEE Trans. Industr. Inf. 18(11), 7920\u20137934 (2022). https:\/\/doi.org\/10.1109\/TII.2022.3167663","journal-title":"IEEE Trans. Industr. Inf."},{"doi-asserted-by":"publisher","unstructured":"Luong, T.D., Tien, V.M., Anh, H.T., Luyen, N.V., Vy, N.C., Duy, P.T., Pham, V.H.: Fedchain: A collaborative framework for building artificial intelligence models using blockchain and federated learning. In: 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), pp. 149\u2013154 (2021). https:\/\/doi.org\/10.1109\/NICS54270.2021.9701450","key":"5247_CR32","DOI":"10.1109\/NICS54270.2021.9701450"},{"issue":"1","key":"5247_CR33","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/MNET.011.2000263","volume":"35","author":"Y Li","year":"2021","unstructured":"Li, Y., Chen, C., Liu, N., Huang, H., Zheng, Z., Yan, Q.: A blockchain-based decentralized federated learning framework with committee consensus. IEEE Netw. 35(1), 234\u2013241 (2021). https:\/\/doi.org\/10.1109\/MNET.011.2000263","journal-title":"IEEE Netw."},{"doi-asserted-by":"publisher","unstructured":"Van Ngan, L., Hoang Tuan, A., Phan The, D., Vo, T.K., Pham, V.H.: A privacy-preserving approach for building learning models in smart healthcare using blockchain and federated learning. In: Proceedings of the 11th International Symposium on Information and Communication Technology, SoICT \u201922, p. 435\u2013441. Association for Computing Machinery, New York, NY (2022). https:\/\/doi.org\/10.1145\/3568562.3568665","key":"5247_CR34","DOI":"10.1145\/3568562.3568665"},{"issue":"11","key":"5247_CR35","doi-asserted-by":"publisher","first-page":"8356","DOI":"10.1109\/TII.2022.3168011","volume":"18","author":"A Yazdinejad","year":"2022","unstructured":"Yazdinejad, A., Dehghantanha, A., Parizi, R.M., Hammoudeh, M., Karimipour, H., Srivastava, G.: Block hunter: federated learning for cyber threat hunting in blockchain-based iiot networks. IEEE Trans. Industr. Inf. 18(11), 8356\u20138366 (2022). https:\/\/doi.org\/10.1109\/TII.2022.3168011","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"1","key":"5247_CR36","doi-asserted-by":"publisher","first-page":"2197173","DOI":"10.1080\/09540091.2023.2197173","volume":"35","author":"M Xu","year":"2023","unstructured":"Xu, M., Li, X.: Fedg2l: a privacy-preserving federated learning scheme base on g2l against poisoning attack. Connect. Sci. 35(1), 2197173 (2023). https:\/\/doi.org\/10.1080\/09540091.2023.2197173","journal-title":"Connect. Sci."},{"issue":"12","key":"5247_CR37","doi-asserted-by":"publisher","first-page":"4783","DOI":"10.1109\/tpds.2022.3202887","volume":"33","author":"C Che","year":"2022","unstructured":"Che, C., Li, X., Chen, C., He, X., Zheng, Z.: A decentralized federated learning framework via committee mechanism with convergence guarantee. IEEE Trans. Parallel Distrib. Syst. 33(12), 4783\u20134800 (2022). https:\/\/doi.org\/10.1109\/tpds.2022.3202887","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"7","key":"5247_CR38","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1109\/TPDS.2020.3044223","volume":"32","author":"M Shayan","year":"2021","unstructured":"Shayan, M., Fung, C., Yoon, C.J.M., Beschastnikh, I.: Biscotti: a blockchain system for private and secure federated learning. IEEE Trans. Parallel Distrib. Syst. 32(7), 1513\u20131525 (2021). https:\/\/doi.org\/10.1109\/TPDS.2020.3044223","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"unstructured":"Jeong, H., Son, H., Lee, S., Hyun, J., Chung, T.M.: Fedcc: Robust federated learning against model poisoning attacks (2022)","key":"5247_CR39"},{"doi-asserted-by":"crossref","unstructured":"Li, J., Shao, Y., Wei, K., Ding, M., Ma, C., Shi, L., Han, Z., Poor, H.V.: Blockchain assisted decentralized federated learning (blade-fl): performance analysis and resource allocation (2021)","key":"5247_CR40","DOI":"10.1109\/TPDS.2021.3138848"},{"key":"5247_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCC.2024.3372814","volume":"12","author":"Y Zhao","year":"2024","unstructured":"Zhao, Y., Qu, Y., Xiang, Y., Chen, F., Gao, L.: Context-aware consensus algorithm for blockchain-empowered federated learning. IEEE Trans. Cloud Comput. 12, 1\u201313 (2024). https:\/\/doi.org\/10.1109\/TCC.2024.3372814","journal-title":"IEEE Trans. Cloud Comput."},{"issue":"7","key":"5247_CR42","doi-asserted-by":"publisher","first-page":"5898","DOI":"10.1109\/JIOT.2023.3237893","volume":"10","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Wang, J., Yan, Z., Wan, Z., J\u00e4ntti, R.: A survey on blockchain-based trust management for internet of things. IEEE Internet Things J. 10(7), 5898\u20135922 (2023). https:\/\/doi.org\/10.1109\/JIOT.2023.3237893","journal-title":"IEEE Internet Things J."},{"key":"5247_CR43","doi-asserted-by":"publisher","first-page":"21127","DOI":"10.1109\/ACCESS.2020.2969820","volume":"8","author":"E Bellini","year":"2020","unstructured":"Bellini, E., Iraqi, Y., Damiani, E.: Blockchain-based distributed trust and reputation management systems: a survey. IEEE Access 8, 21127\u201321151 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2969820","journal-title":"IEEE Access"},{"key":"5247_CR44","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.future.2019.08.005","volume":"102","author":"EK Wang","year":"2020","unstructured":"Wang, E.K., Liang, Z., Chen, C.M., Kumari, S., Khan, M.K.: Porx: a reputation incentive scheme for blockchain consensus of iiot. Futur. Gener. Comput. Syst. 102, 140\u2013151 (2020). https:\/\/doi.org\/10.1016\/j.future.2019.08.005","journal-title":"Futur. Gener. Comput. Syst."},{"doi-asserted-by":"publisher","unstructured":"Bao, X., Su, C., Xiong, Y., Huang, W., Hu, Y.: Flchain: A blockchain for auditable federated learning with trust and incentive. In: 2019 5th International Conference on Big Data Computing and Communications (BIGCOM), pp. 151\u2013159 (2019). https:\/\/doi.org\/10.1109\/BIGCOM.2019.00030","key":"5247_CR45","DOI":"10.1109\/BIGCOM.2019.00030"},{"issue":"11","key":"5247_CR46","doi-asserted-by":"publisher","first-page":"7696","DOI":"10.1109\/TII.2022.3140806","volume":"18","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., He, N., Li, Q., Wang, K., Gao, H., Gao, T.: Detectpmfl: privacy-preserving momentum federated learning considering unreliable industrial agents. IEEE Trans. Industr. Inf. 18(11), 7696\u20137706 (2022). https:\/\/doi.org\/10.1109\/TII.2022.3140806","journal-title":"IEEE Trans. Industr. Inf."},{"unstructured":"McMahan, H.B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data (2023)","key":"5247_CR47"},{"unstructured":"Ongaro, D., Ousterhout, J.: In search of an understandable consensus algorithm. In: Proceedings of the 2014 USENIX Conference on USENIX Annual Technical Conference, USENIX ATC\u201914, p. 305\u2013320. USENIX Association (2014)","key":"5247_CR48"},{"key":"5247_CR49","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1910.08510","author":"J Niu","year":"2019","unstructured":"Niu, J., Feng, C., Dau, H., Huang, Y.C., Zhu, J.: Analysis of nakamoto consensus, revisited. Cryptology (2019). https:\/\/doi.org\/10.48550\/arXiv.1910.08510","journal-title":"Cryptology"},{"key":"5247_CR50","doi-asserted-by":"publisher","DOI":"10.3390\/s23135941","author":"ECP Neto","year":"2023","unstructured":"Neto, E.C.P., Dadkhah, S., Ferreira, R., Zohourian, A., Lu, R., Ghorbani, A.A.: Ciciot 2023: a real-time dataset and benchmark for large-scale attacks in IoT environment. Sensors (2023). https:\/\/doi.org\/10.3390\/s23135941","journal-title":"Sensors"},{"doi-asserted-by":"publisher","unstructured":"Chuanxin, Z., Yi, S., Degang, W.: Federated learning with gaussian differential privacy. In: Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI \u201920, p. 296\u2013301. Association for Computing Machinery, New York, NY (2020). https:\/\/doi.org\/10.1145\/3438872.3439097","key":"5247_CR51","DOI":"10.1145\/3438872.3439097"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05247-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-025-05247-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-025-05247-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T21:23:09Z","timestamp":1758144189000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-025-05247-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,30]]},"references-count":51,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["5247"],"URL":"https:\/\/doi.org\/10.1007\/s10586-025-05247-7","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"type":"print","value":"1386-7857"},{"type":"electronic","value":"1573-7543"}],"subject":[],"published":{"date-parts":[[2025,8,30]]},"assertion":[{"value":"1 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"571"}}