{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:32:58Z","timestamp":1784820778940,"version":"3.55.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031703614","type":"print"},{"value":"9783031703621","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-70362-1_4","type":"book-chapter","created":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T03:03:03Z","timestamp":1724900583000},"page":"55-72","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Fast-FedUL: A Training-Free Federated Unlearning with\u00a0Provable Skew Resilience"],"prefix":"10.1007","author":[{"given":"Thanh Trung","family":"Huynh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trong Bang","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phi Le","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thanh Tam","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"Weidlich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quoc Viet Hung","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl","family":"Aberer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Bourtoule, L., et\u00a0al.: Machine unlearning. In: SP, pp. 141\u2013159 (2021)","DOI":"10.1109\/SP40001.2021.00019"},{"key":"4_CR2","doi-asserted-by":"crossref","unstructured":"Cao, X., et al.: MPAF: model poisoning attacks to federated learning based on fake clients. In: Proceedings of the IEEE\/CVF CVPR, pp. 3396\u20133404 (2022)","DOI":"10.1109\/CVPRW56347.2022.00383"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Cao, Y., et\u00a0al.: Towards making systems forget with machine unlearning. In: SP, pp. 463\u2013480 (2015)","DOI":"10.1109\/SP.2015.35"},{"key":"4_CR4","doi-asserted-by":"crossref","unstructured":"Cha, S., et\u00a0al.: Learning to unlearn: instance-wise unlearning for pre-trained classifiers. arXiv preprint arXiv:2301.11578 (2023)","DOI":"10.1609\/aaai.v38i10.28996"},{"key":"4_CR5","unstructured":"Che, T., et\u00a0al.: Fast federated machine unlearning with nonlinear functional theory. In: International Conference on Machine Learning, pp. 4241\u20134268. PMLR (2023)"},{"key":"4_CR6","unstructured":"Chien, E., Pan, C., Milenkovic, O.: Efficient model updates for approximate unlearning of graph-structured data. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Golatkar, A., et\u00a0al.: Eternal sunshine of the spotless net: selective forgetting in deep networks. In: CVPR, pp. 9304\u20139312 (2020)","DOI":"10.1109\/CVPR42600.2020.00932"},{"key":"4_CR8","unstructured":"Halimi, A., et\u00a0al.: Federated unlearning: how to efficiently erase a client in FL? arXiv preprint arXiv:2207.05521 (2022)"},{"key":"4_CR9","unstructured":"Horv\u00e1th, S., Richt\u00e1rik, P.: Nonconvex variance reduced optimization with arbitrary sampling. In: ICLR, pp. 2781\u20132789 (2019)"},{"key":"4_CR10","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Technical report\u00a00, University of Toronto, Toronto, Ontario (2009)"},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/978-3-030-86236-7_6","volume-title":"Recent Advances in Industrial and Applied Mathematics","author":"K Lauter","year":"2022","unstructured":"Lauter, K.: Private AI: machine learning on encrypted data. In: Chac\u00f3n Rebollo, T., Donat, R., Higueras, I. (eds.) Recent Advances in Industrial and Applied Mathematics, pp. 97\u2013113. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-86236-7_6"},{"key":"4_CR12","unstructured":"LeCun, Y.: The MNIST database of handwritten digits (1998). http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"4_CR13","doi-asserted-by":"publisher","first-page":"30039","DOI":"10.1109\/ACCESS.2022.3159694","volume":"10","author":"JW Lee","year":"2022","unstructured":"Lee, J.W., et al.: Privacy-preserving machine learning with fully homomorphic encryption for deep neural network. IEEE Access 10, 30039\u201330054 (2022)","journal-title":"IEEE Access"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Li, Q., et al.: Federated learning on non-IID data silos: an experimental study. In: ICDE, pp. 965\u2013978 (2022)","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"4_CR15","unstructured":"Li, Y., Chen, C., Zheng, X., Zhang, J.: Federated unlearning via active forgetting. arXiv preprint arXiv:2307.03363 (2023)"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Liu, G., Ma, X., Yang, Y., Wang, C., Liu, J.: Federaser: enabling efficient client-level data removal from federated learning models. In: IWQOS, pp. 1\u201310 (2021)","DOI":"10.1109\/IWQOS52092.2021.9521274"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Y., et\u00a0al.: The right to be forgotten in federated learning: an efficient realization with rapid retraining. In: IEEE INFOCOM, pp. 1749\u20131758 (2022)","DOI":"10.1109\/INFOCOM48880.2022.9796721"},{"key":"4_CR18","unstructured":"McMahan, B., et\u00a0al.: Communication-efficient learning of deep networks from decentralized data. In: AISTATS, pp. 1273\u20131282 (2017)"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Mehta, R., Pal, S., Singh, V., Ravi, S.N.: Deep unlearning via randomized conditionally independent hessians. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10422\u201310431 (2022)","DOI":"10.1109\/CVPR52688.2022.01017"},{"key":"4_CR20","unstructured":"Regulation, P.: Regulation (EU) 2016\/679 of the European parliament and of the council. Regulation (EU) 679, 2016 (2016)"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Voigt, P., et al.: The EU General Data Protection Regulation (GDPR). A Practical Guide, 1st edn. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-57959-7","DOI":"10.1007\/978-3-319-57959-7"},{"key":"4_CR22","unstructured":"Wang, H., et al.: Attack of the tails: yes, you really can backdoor federated learning. In: NIPS, vol. 33, pp. 16070\u201316084 (2020)"},{"key":"4_CR23","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Federated unlearning via class-discriminative pruning. In: WWW, pp. 622\u2013632 (2022)","DOI":"10.1145\/3485447.3512222"},{"key":"4_CR24","doi-asserted-by":"crossref","unstructured":"Wang, W., et\u00a0al.: BFU: Bayesian federated unlearning with parameter self-sharing. In: Proceedings of the 2023 ACM ASIACCS, pp. 567\u2013578 (2023)","DOI":"10.1145\/3579856.3590327"},{"key":"4_CR25","unstructured":"Wang, Y., Lin, L., Chen, J.: Communication-efficient adaptive federated learning. In: International Conference on Machine Learning, pp. 22802\u201322838. PMLR (2022)"},{"key":"4_CR26","unstructured":"Wu, C., Zhu, S., Mitra, P.: Federated unlearning with knowledge distillation. arXiv preprint arXiv:2201.09441 (2022)"},{"key":"4_CR27","unstructured":"Wu, Y., et\u00a0al.: Deltagrad: rapid retraining of machine learning models. In: International Conference on Machine Learning, pp. 10355\u201310366 (2020)"},{"key":"4_CR28","unstructured":"Xie, C., et\u00a0al.: DBA: distributed backdoor attacks against federated learning. In: ICLR (2019)"},{"key":"4_CR29","unstructured":"Yang, J., et\u00a0al.: MedMNIST v2: a large-scale lightweight benchmark for 2D and 3D biomedical image classification. arXiv preprint arXiv:2110.14795 (2021)"},{"issue":"100","key":"4_CR30","first-page":"1","volume":"24","author":"D Zeng","year":"2023","unstructured":"Zeng, D., Liang, S., Hu, X., Wang, H., Xu, Z.: FedLab: a flexible federated learning framework. J. Mach. Learn. Res. 24(100), 1\u20137 (2023)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70362-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T17:49:06Z","timestamp":1756921746000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70362-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031703614","9783031703621"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70362-1_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"22 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}