{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T22:19:34Z","timestamp":1730326774407,"version":"3.28.0"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,7,10]]},"DOI":"10.1145\/3579856.3590327","type":"proceedings-article","created":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T14:52:13Z","timestamp":1688568733000},"page":"567-578","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["BFU: Bayesian Federated Unlearning with Parameter Self-Sharing"],"prefix":"10.1145","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6123-1457","authenticated-orcid":false,"given":"Weiqi","family":"Wang","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8905-0941","authenticated-orcid":false,"given":"Zhiyi","family":"Tian","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2352-0485","authenticated-orcid":false,"given":"Chenhan","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Australia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6368-576X","authenticated-orcid":false,"given":"An","family":"Liu","sequence":"additional","affiliation":[{"name":"Soochow University, Taiwan"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4485-6743","authenticated-orcid":false,"given":"Shui","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Australia"}]}],"member":"320","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Machine learning 28, 1","author":"Baxter Jonathan","year":"1997","unstructured":"Jonathan Baxter . 1997. A Bayesian\/information theoretic model of learning to learn via multiple task sampling. Machine learning 28, 1 ( 1997 ), 7\u201339. Jonathan Baxter. 1997. A Bayesian\/information theoretic model of learning to learn via multiple task sampling. Machine learning 28, 1 (1997), 7\u201339."},{"volume-title":"Bayesian theory. Vol.\u00a0405","author":"Bernardo M","key":"e_1_3_2_1_2_1","unstructured":"Jos\u00e9\u00a0 M Bernardo and Adrian\u00a0 FM Smith . 2009. Bayesian theory. Vol.\u00a0405 . John Wiley & Sons . Jos\u00e9\u00a0M Bernardo and Adrian\u00a0FM Smith. 2009. Bayesian theory. Vol.\u00a0405. John Wiley & Sons."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00019"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2015.35"},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the Tenth International Conference on Machine Learning. Citeseer, 41\u201348","author":"Caruana R","year":"1993","unstructured":"R Caruana . 1993 . Multitask learning: A knowledge-based source of inductive bias1 . In Proceedings of the Tenth International Conference on Machine Learning. Citeseer, 41\u201348 . R Caruana. 1993. Multitask learning: A knowledge-based source of inductive bias1. In Proceedings of the Tenth International Conference on Machine Learning. Citeseer, 41\u201348."},{"key":"e_1_3_2_1_6_1","volume-title":"Fedbe: Making bayesian model ensemble applicable to federated learning. arXiv preprint arXiv:2009.01974","author":"Chen Hong-You","year":"2020","unstructured":"Hong-You Chen and Wei-Lun Chao . 2020 . Fedbe: Making bayesian model ensemble applicable to federated learning. arXiv preprint arXiv:2009.01974 (2020). Hong-You Chen and Wei-Lun Chao. 2020. Fedbe: Making bayesian model ensemble applicable to federated learning. arXiv preprint arXiv:2009.01974 (2020)."},{"key":"e_1_3_2_1_7_1","volume-title":"Knowledge Removal in Sampling-based Bayesian Inference. In The Tenth International Conference on Learning Representations, ICLR 2022","author":"Fu Shaopeng","year":"2022","unstructured":"Shaopeng Fu , Fengxiang He , and Dacheng Tao . 2022 . Knowledge Removal in Sampling-based Bayesian Inference. In The Tenth International Conference on Learning Representations, ICLR 2022 , Virtual Event , April 25-29, 2022. OpenReview.net. https:\/\/openreview.net\/forum?id=dTqOcTUOQO Shaopeng Fu, Fengxiang He, and Dacheng Tao. 2022. Knowledge Removal in Sampling-based Bayesian Inference. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net. https:\/\/openreview.net\/forum?id=dTqOcTUOQO"},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 792\u2013801","author":"Golatkar Aditya","year":"2021","unstructured":"Aditya Golatkar , Alessandro Achille , Avinash Ravichandran , Marzia Polito , and Stefano Soatto . 2021 . Mixed-privacy forgetting in deep networks . In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 792\u2013801 . Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto. 2021. Mixed-privacy forgetting in deep networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 792\u2013801."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00932"},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020","author":"Guo Chuan","year":"2020","unstructured":"Chuan Guo , Tom Goldstein , Awni\u00a0 Y. Hannun , and Laurens van\u00a0der Maaten . 2020 . Certified Data Removal from Machine Learning Models . In Proceedings of the 37th International Conference on Machine Learning, ICML 2020 , 13-18 July 2020, Virtual Event(Proceedings of Machine Learning Research, Vol.\u00a0119). PMLR, 3832\u20133842. Chuan Guo, Tom Goldstein, Awni\u00a0Y. Hannun, and Laurens van\u00a0der Maaten. 2020. Certified Data Removal from Machine Learning Models. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event(Proceedings of Machine Learning Research, Vol.\u00a0119). PMLR, 3832\u20133842."},{"key":"e_1_3_2_1_11_1","volume-title":"Federated Unlearning: How to Efficiently Erase a Client in FL?arXiv preprint arXiv:2207.05521","author":"Halimi Anisa","year":"2022","unstructured":"Anisa Halimi , Swanand Kadhe , Ambrish Rawat , and Nathalie Baracaldo . 2022 . Federated Unlearning: How to Efficiently Erase a Client in FL?arXiv preprint arXiv:2207.05521 (2022). Anisa Halimi, Swanand Kadhe, Ambrish Rawat, and Nathalie Baracaldo. 2022. Federated Unlearning: How to Efficiently Erase a Client in FL?arXiv preprint arXiv:2207.05521 (2022)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/532"},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 7482\u20137491","author":"Kendall Alex","year":"2018","unstructured":"Alex Kendall , Yarin Gal , and Roberto Cipolla . 2018 . Multi-task learning using uncertainty to weigh losses for scene geometry and semantics . In Proceedings of the IEEE conference on computer vision and pattern recognition. 7482\u20137491 . Alex Kendall, Yarin Gal, and Roberto Cipolla. 2018. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7482\u20137491."},{"key":"e_1_3_2_1_14_1","first-page":"19757","article-title":"Knowledge-adaptation priors","volume":"34","author":"Khan Mohammad","year":"2021","unstructured":"Mohammad Emtiyaz\u00a0E Khan and Siddharth Swaroop . 2021 . Knowledge-adaptation priors . Advances in Neural Information Processing Systems 34 (2021), 19757 \u2013 19770 . Mohammad Emtiyaz\u00a0E Khan and Siddharth Swaroop. 2021. Knowledge-adaptation priors. Advances in Neural Information Processing Systems 34 (2021), 19757\u201319770.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_15_1","volume-title":"Convolutional deep belief networks on cifar-10. Unpublished manuscript 40, 7","author":"Krizhevsky Alex","year":"2010","unstructured":"Alex Krizhevsky and Geoff Hinton . 2010. Convolutional deep belief networks on cifar-10. Unpublished manuscript 40, 7 ( 2010 ), 1\u20139. Alex Krizhevsky and Geoff Hinton. 2010. Convolutional deep belief networks on cifar-10. Unpublished manuscript 40, 7 (2010), 1\u20139."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_17_1","volume-title":"Federated unlearning. arXiv preprint arXiv:2012.13891","author":"Liu Gaoyang","year":"2020","unstructured":"Gaoyang Liu , Xiaoqiang Ma , Yang Yang , Chen Wang , and Jiangchuan Liu . 2020. Federated unlearning. arXiv preprint arXiv:2012.13891 ( 2020 ). Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu. 2020. Federated unlearning. arXiv preprint arXiv:2012.13891 (2020)."},{"key":"e_1_3_2_1_18_1","volume-title":"2021 IEEE\/ACM 29th International Symposium on Quality of Service (IWQOS). IEEE, 1\u201310","author":"Liu Gaoyang","year":"2021","unstructured":"Gaoyang Liu , Xiaoqiang Ma , Yang Yang , Chen Wang , and Jiangchuan Liu . 2021 . Federaser: Enabling efficient client-level data removal from federated learning models . In 2021 IEEE\/ACM 29th International Symposium on Quality of Service (IWQOS). IEEE, 1\u201310 . Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu. 2021. Federaser: Enabling efficient client-level data removal from federated learning models. In 2021 IEEE\/ACM 29th International Symposium on Quality of Service (IWQOS). IEEE, 1\u201310."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796721"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmp.2017.05.006"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clsr.2013.03.010"},{"key":"e_1_3_2_1_22_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise\u00a0Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273\u20131282. Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise\u00a0Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273\u20131282."},{"key":"e_1_3_2_1_23_1","volume-title":"Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629","author":"McMahan H\u00a0Brendan","year":"2016","unstructured":"H\u00a0Brendan McMahan , Eider Moore , Daniel Ramage , and Blaise\u00a0Ag\u00fcera y Arcas . 2016. Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629 ( 2016 ). H\u00a0Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise\u00a0Ag\u00fcera y Arcas. 2016. Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629 (2016)."},{"key":"e_1_3_2_1_24_1","volume-title":"Descent-to-delete: Gradient-based methods for machine unlearning. In Algorithmic Learning Theory. PMLR, 931\u2013962.","author":"Neel Seth","year":"2021","unstructured":"Seth Neel , Aaron Roth , and Saeed Sharifi-Malvajerdi . 2021 . Descent-to-delete: Gradient-based methods for machine unlearning. In Algorithmic Learning Theory. PMLR, 931\u2013962. Seth Neel, Aaron Roth, and Saeed Sharifi-Malvajerdi. 2021. Descent-to-delete: Gradient-based methods for machine unlearning. In Algorithmic Learning Theory. PMLR, 931\u2013962."},{"key":"e_1_3_2_1_25_1","first-page":"16025","article-title":"Variational bayesian unlearning","volume":"33","author":"Nguyen Quoc\u00a0Phong","year":"2020","unstructured":"Quoc\u00a0Phong Nguyen , Bryan Kian\u00a0Hsiang Low , and Patrick Jaillet . 2020 . Variational bayesian unlearning . Advances in Neural Information Processing Systems 33 (2020), 16025 \u2013 16036 . Quoc\u00a0Phong Nguyen, Bryan Kian\u00a0Hsiang Low, and Patrick Jaillet. 2020. Variational bayesian unlearning. Advances in Neural Information Processing Systems 33 (2020), 16025\u201316036.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_26_1","volume-title":"Remember what you want to forget: Algorithms for machine unlearning. Advances in Neural Information Processing Systems 34","author":"Sekhari Ayush","year":"2021","unstructured":"Ayush Sekhari , Jayadev Acharya , Gautam Kamath , and Ananda\u00a0Theertha Suresh . 2021. Remember what you want to forget: Algorithms for machine unlearning. Advances in Neural Information Processing Systems 34 ( 2021 ). Ayush Sekhari, Jayadev Acharya, Gautam Kamath, and Ananda\u00a0Theertha Suresh. 2021. Remember what you want to forget: Algorithms for machine unlearning. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_1_27_1","volume-title":"Multi-task learning as multi-objective optimization. Advances in neural information processing systems 31","author":"Sener Ozan","year":"2018","unstructured":"Ozan Sener and Vladlen Koltun . 2018. Multi-task learning as multi-objective optimization. Advances in neural information processing systems 31 ( 2018 ). Ozan Sener and Vladlen Koltun. 2018. Multi-task learning as multi-objective optimization. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the ACM Web Conference","author":"Wang Junxiao","year":"2022","unstructured":"Junxiao Wang , Song Guo , Xin Xie , and Heng Qi . 2022 . Federated unlearning via class-discriminative pruning . In Proceedings of the ACM Web Conference 2022. 622\u2013632. Junxiao Wang, Song Guo, Xin Xie, and Heng Qi. 2022. Federated unlearning via class-discriminative pruning. In Proceedings of the ACM Web Conference 2022. 622\u2013632."},{"key":"e_1_3_2_1_29_1","volume-title":"Federated Unlearning with Knowledge Distillation. arXiv preprint arXiv:2201.09441","author":"Wu Chen","year":"2022","unstructured":"Chen Wu , Sencun Zhu , and Prasenjit Mitra . 2022. Federated Unlearning with Knowledge Distillation. arXiv preprint arXiv:2201.09441 ( 2022 ). Chen Wu, Sencun Zhu, and Prasenjit Mitra. 2022. Federated Unlearning with Knowledge Distillation. arXiv preprint arXiv:2201.09441 (2022)."},{"key":"e_1_3_2_1_30_1","volume-title":"International Conference on Machine Learning. PMLR, 10355\u201310366","author":"Wu Yinjun","year":"2020","unstructured":"Yinjun Wu , Edgar Dobriban , and Susan Davidson . 2020 . Deltagrad: Rapid retraining of machine learning models . In International Conference on Machine Learning. PMLR, 10355\u201310366 . Yinjun Wu, Edgar Dobriban, and Susan Davidson. 2020. Deltagrad: Rapid retraining of machine learning models. In International Conference on Machine Learning. PMLR, 10355\u201310366."},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 2020 SIAM International Conference on Data Mining. SIAM, 199\u2013207","author":"Xu Peng","year":"2020","unstructured":"Peng Xu , Fred Roosta , and Michael\u00a0 W Mahoney . 2020 . Second-order optimization for non-convex machine learning: An empirical study . In Proceedings of the 2020 SIAM International Conference on Data Mining. SIAM, 199\u2013207 . Peng Xu, Fred Roosta, and Michael\u00a0W Mahoney. 2020. Second-order optimization for non-convex machine learning: An empirical study. In Proceedings of the 2020 SIAM International Conference on Data Mining. SIAM, 199\u2013207."},{"key":"e_1_3_2_1_32_1","volume-title":"International Conference on Machine Learning. PMLR, 26293\u201326310","author":"Zhang Xu","year":"2022","unstructured":"Xu Zhang , Yinchuan Li , Wenpeng Li , Kaiyang Guo , and Yunfeng Shao . 2022 . Personalized federated learning via variational bayesian inference . In International Conference on Machine Learning. PMLR, 26293\u201326310 . Xu Zhang, Yinchuan Li, Wenpeng Li, Kaiyang Guo, and Yunfeng Shao. 2022. Personalized federated learning via variational bayesian inference. In International Conference on Machine Learning. PMLR, 26293\u201326310."}],"event":{"name":"ASIA CCS '23: ACM ASIA Conference on Computer and Communications Security","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"],"location":"Melbourne VIC Australia","acronym":"ASIA CCS '23"},"container-title":["Proceedings of the ACM Asia Conference on Computer and Communications Security"],"original-title":[],"deposited":{"date-parts":[[2023,7,5]],"date-time":"2023-07-05T15:27:05Z","timestamp":1688570825000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579856.3590327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,10]]},"references-count":32,"alternative-id":["10.1145\/3579856.3590327","10.1145\/3579856"],"URL":"https:\/\/doi.org\/10.1145\/3579856.3590327","relation":{},"subject":[],"published":{"date-parts":[[2023,7,10]]},"assertion":[{"value":"2023-07-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}