{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T01:00:30Z","timestamp":1760576430145,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819531813","type":"print"},{"value":"9789819531820","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-3182-0_29","type":"book-chapter","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T19:17:36Z","timestamp":1760555856000},"page":"459-473","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FedVoD: A Robust Federated Learning Defense Strategy Against Hybrid Byzantine Attacks"],"prefix":"10.1007","author":[{"given":"Hongjie","family":"Luo","sequence":"first","affiliation":[]},{"given":"Yuling","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Dou","sequence":"additional","affiliation":[]},{"given":"Junyu","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Haonan","family":"Zhan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"29_CR1","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Singh, A., Zhu, J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Volume\u00a054 of Proceedings of Machine Learning Research, pp. 1273\u20131282. PMLR (2017)"},{"key":"29_CR2","doi-asserted-by":"crossref","unstructured":"Yang, Q., Liu, Y., Chen T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2) (2019)","DOI":"10.1145\/3298981"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Huang, W., et al.: Federated learning for generalization, robustness, fairness: a survey and benchmark. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3418862"},{"key":"29_CR4","unstructured":"Blanchard, P., El Mhamdi, E. M., Guerraoui, R., Stainer, J.: Machine learning with adversaries: Byzantine tolerant gradient descent. In:\u00a0Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, Vol.\u00a030. Curran Associates, Inc. (2017)"},{"key":"29_CR5","unstructured":"Yin, D., Chen, Y., Ramchandran, K., Bartlett, P.: Byzantine-robust distributed learning: towards optimal statistical rates. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, Volume\u00a080 of Proceedings of Machine Learning Research, pp. 5650\u20135659. PMLR (2018)"},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Shejwalkar, V., Houmansadr, A.: Manipulating the byzantine: optimizing model poisoning attacks and defenses for federated learning (2021)","DOI":"10.14722\/ndss.2021.24498"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Cao, X., Fang, M., Liu, J., Gong, N.Z.: FLTrust: byzantine-robust federated learning via trust bootstrapping. arXiv preprint: arXiv:2012.13995 (2020)","DOI":"10.14722\/ndss.2021.24434"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Tang, P., Zhu, X., Qiu, W., Huang, Z., Mu, Z., Li, S.: FLAD: byzantine-robust federated learning based on gradient feature anomaly detection. IEEE Trans. Dependable Secure Comput., 1\u201317 (2025)","DOI":"10.1109\/TDSC.2025.3542437"},{"key":"29_CR9","doi-asserted-by":"publisher","first-page":"8747","DOI":"10.1109\/TIFS.2024.3461449","volume":"19","author":"W Minzhe","year":"2024","unstructured":"Minzhe, W., Zhao, B., Xiao, Y., Deng, C., Liu, Y., Liu, X.: Model: a model poisoning defense framework for federated learning via truth discovery. IEEE Trans. Inf. Forensics Secur. 19, 8747\u20138759 (2024)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Qin, Z., Chen, F., Zhi, C., Yan, X., Deng, S.: Resisting backdoor attacks in federated learning via bidirectional elections and individual perspective. In: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence, AAAI\u201924\/IAAI\u201924\/EAAI\u201924. AAAI Press (2024)","DOI":"10.1609\/aaai.v38i13.29385"},{"key":"29_CR11","unstructured":"Xie, C., Koyejo, O., Gupta, I.: Fall of empires: breaking byzantine-tolerant SGD by inner product manipulation. In: Adams, R.P., Gogate, V. (eds.) Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, Volume 115 of Proceedings of Machine Learning Research, pp. 261\u2013270. PMLR (2020)"},{"key":"29_CR12","unstructured":"Fang, M., Cao, X., Jia, J., Gong, N.: Local model poisoning attacks to byzantine-robust federated learning. In: Proceedings of the 29th USENIX Conference on Security Symposium, SEC\u201920. USENIX Association, USA (2020)"},{"key":"29_CR13","unstructured":"Baruch, G., Baruch, M., Goldberg, Y.: A little is enough: circumventing defenses for distributed learning. In:\u00a0Wallach, H.,\u00a0Larochelle, H.,\u00a0Beygelzimer, A.,\u00a0d\u2019Alch\u00e9-Buc, F.,\u00a0Fox, E.,\u00a0Garnett, R. (eds.) Advances in Neural Information Processing Systems, Volume\u00a032. Curran Associates, Inc., (2019)"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: 3DFed: adaptive and extensible framework for covert backdoor attack in federated learning. In: 2023 IEEE Symposium on Security and Privacy (SP), pp. 1893\u20131907 (2023)","DOI":"10.1109\/SP46215.2023.10179401"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Xu, J., Huang, S.-L., Song, L., Lan, T.: Byzantine-robust federated learning through collaborative malicious gradient filtering. In: 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS), pp. 1223\u20131235 (2022)","DOI":"10.1109\/ICDCS54860.2022.00120"},{"key":"29_CR16","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, WWW \u201924, pp. 2914\u20132925. Association for Computing Machinery, New York (2024)","DOI":"10.1145\/3589334.3645425"},{"key":"29_CR17","unstructured":"EMhamdi, E.M.E., Guerraoui, R., Rouault, S.: The hidden vulnerability of distributed learning in Byzantium. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, Volume\u00a080 of Proceedings of Machine Learning Research, pp. 3521\u20133530. PMLR (2018)"},{"issue":"6","key":"29_CR18","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1109\/MSP.2012.2211477","volume":"29","author":"L Deng","year":"2012","unstructured":"Deng, L.: The MNIST database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Process. Mag. 29(6), 141\u2013142 (2012)","journal-title":"IEEE Signal Process. Mag."},{"key":"29_CR19","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. CoRR, abs\/1708.07747 (2017)"},{"key":"29_CR20","unstructured":"Krizhevsky, A.,\u00a0Hinton, G.: Learning multiple layers of features from tiny images. Handb. Syst. Autoimmune Diseases 1(4) (2009)"},{"key":"29_CR21","doi-asserted-by":"crossref","unstructured":"Qin, Z., Deng, S., Zhao, M., Yan, X.: FedAPEN: personalized cross-silo federated learning with adaptability to statistical heterogeneity. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD \u201923, pp. 1954\u20131964. Association for Computing Machinery, New York (2023)","DOI":"10.1145\/3580305.3599344"}],"container-title":["Lecture Notes in Computer Science","Data Security and Privacy Protection"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3182-0_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T19:17:39Z","timestamp":1760555859000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3182-0_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"ISBN":["9789819531813","9789819531820"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3182-0_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,15]]},"assertion":[{"value":"15 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DSPP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Security and Privacy Protection","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dspp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dspp2025.xidian.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}