{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:03:17Z","timestamp":1784131397259,"version":"3.55.0"},"reference-count":42,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,2]]},"DOI":"10.1109\/infocom48880.2022.9796721","type":"proceedings-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T21:18:49Z","timestamp":1655759929000},"page":"1749-1758","source":"Crossref","is-referenced-by-count":187,"title":["The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining"],"prefix":"10.1109","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"City University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingliang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Monash University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898717761"},{"key":"ref38","first-page":"26","article-title":"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude","volume":"4","author":"tieleman","year":"2012","journal-title":"COURSERA Neural Networks for Machine Learning"},{"key":"ref33","volume":"1","author":"friedman","year":"2001","journal-title":"The Elements of Statistical Learning"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref31","article-title":"A progressive batching l-bfgs method for machine learning","author":"bollapragada","year":"2018","journal-title":"Proc of ICML"},{"key":"ref30","article-title":"A multi-batch l-bfgs method for machine learning","author":"berahas","year":"2016","journal-title":"Proc of NeurIPS"},{"key":"ref37","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc of ICLR"},{"key":"ref36","article-title":"Asynchronous stochastic gradient descent with delay compensation","author":"zheng","year":"2017","journal-title":"Proc of ICML"},{"key":"ref35","first-page":"29","article-title":"Improving the convergence of back-propagation learning with second order methods","author":"becker","year":"1988","journal-title":"Proceedings of the 1988 Connectionist Models Summer School"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmp.2017.05.006"},{"key":"ref10","article-title":"Deltagrad: Rapid retraining of machine learning models","author":"wu","year":"2020","journal-title":"Proc of ICML"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378171"},{"key":"ref11","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc of AISTATS"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488807"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155414"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488906"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737587"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/IWQOS52092.2021.9521274"},{"key":"ref28","article-title":"Certifiable machine unlearning for linear models","author":"mahadevan","year":"2021","journal-title":"arXiv preprint arXiv 2106 13112"},{"key":"ref4","article-title":"Understanding black-box predictions via influence functions","author":"koh","year":"2017","journal-title":"Proc of ICML"},{"key":"ref27","article-title":"Evaluating gradient inversion attacks and defenses in federated learning","author":"huang","year":"2021","journal-title":"Proc of NeurIPS"},{"key":"ref3","article-title":"Descent-to-delete: Gradient-based methods for machine unlearning","author":"neel","year":"2021","journal-title":"Algorithmic Learning Theory"},{"key":"ref6","article-title":"Making ai forget you: data deletion in machine learning","author":"ginart","year":"2019","journal-title":"Proc of NeurIPS"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1002\/nme.1620141104"},{"key":"ref5","article-title":"Machine unlearning: Linear filtration for logit-based classifiers","author":"baumhauer","year":"2020","journal-title":"arXiv preprint arXiv 2002 05155"},{"key":"ref8","article-title":"Approximate data deletion from machine learning models","author":"izzo","year":"2021","journal-title":"Proc of AISTATS"},{"key":"ref7","article-title":"Certified data removal from machine learning models","author":"guo","year":"2020","journal-title":"Proc of ICLR"},{"key":"ref2","first-page":"3152676","article-title":"The eu general data protection regulation (gdpr)","volume":"10","author":"voigt","year":"2017","journal-title":"A Practical Guide 1st Ed Cham Springer International Publishing"},{"key":"ref9","article-title":"Graph unlearning","author":"chen","year":"2021","journal-title":"Proc of CCS"},{"key":"ref1","article-title":"Machine unlearning","author":"bourtoule","year":"2019","journal-title":"Proc of IEEE S&P"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976236.23"},{"key":"ref22","article-title":"Machine unlearning for random forests","author":"brophy","year":"2021","journal-title":"Proc of ICML"},{"key":"ref21","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v35i12.17275","article-title":"Adahessian: An adaptive second order optimizer for machine learning","author":"yao","year":"2021","journal-title":"Proc Of AAAI"},{"key":"ref42","article-title":"Leaf: A benchmark for federated settings","author":"caldas","year":"2018","journal-title":"arXiv preprint arXiv 1812 08942"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-018-1772-4"},{"key":"ref41","article-title":"Hessian-based analysis of large batch training and robustness to adversaries","author":"yao","year":"2018","journal-title":"Proc of NeurIPS"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457239"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/978-3-030-63076-8_2","article-title":"Deep leakage from gradients","author":"zhu","year":"2020","journal-title":"Federated Learning"},{"key":"ref25","article-title":"On the necessity of auditable algorithmic definitions for machine unlearning","author":"thudi","year":"2021","journal-title":"arXiv preprint arXiv 2110 13189"}],"event":{"name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications","location":"London, United Kingdom","start":{"date-parts":[[2022,5,2]]},"end":{"date-parts":[[2022,5,5]]}},"container-title":["IEEE INFOCOM 2022 - IEEE Conference on Computer Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9796607\/9796652\/09796721.pdf?arnumber=9796721","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T21:41:25Z","timestamp":1675892485000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9796721\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,2]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/infocom48880.2022.9796721","relation":{},"subject":[],"published":{"date-parts":[[2022,5,2]]}}}