{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T20:08:59Z","timestamp":1778789339681,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T00:00:00Z","timestamp":1670198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Henan Key Laboratory of Network Cryptography Technology & State Key Laboratory of Mathematical Engineering and Advanced Computing","award":["LNCT2020-A06"],"award-info":[{"award-number":["LNCT2020-A06"]}]},{"name":"the National Natural Science Foundation of China","award":["62072361, 62125205"],"award-info":[{"award-number":["62072361, 62125205"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["JB211505"],"award-info":[{"award-number":["JB211505"]}]},{"name":"Key Research and Development Program of Shaanxi","award":["2022GY-019"],"award-info":[{"award-number":["2022GY-019"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,12,5]]},"DOI":"10.1145\/3564625.3567973","type":"proceedings-article","created":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T01:01:29Z","timestamp":1670029289000},"page":"159-170","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Compressed Federated Learning Based on Adaptive Local Differential Privacy"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5437-3572","authenticated-orcid":false,"given":"Yinbin","family":"Miao","sequence":"first","affiliation":[{"name":"Xidian University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5506-0367","authenticated-orcid":false,"given":"Rongpeng","family":"Xie","sequence":"additional","affiliation":[{"name":"Xidian University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5583-4155","authenticated-orcid":false,"given":"Xinghua","family":"Li","sequence":"additional","affiliation":[{"name":"Xidian University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4238-3295","authenticated-orcid":false,"given":"Ximeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6023-2864","authenticated-orcid":false,"given":"Zhuo","family":"Ma","sequence":"additional","affiliation":[{"name":"Xidian University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3491-8146","authenticated-orcid":false,"given":"Robert H.","family":"Deng","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,12,5]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_1_2_1","first-page":"1333","article-title":"Privacy-preserving deep learning via additively homomorphic encryption","volume":"13","author":"Aono Yoshinori","year":"2017","unstructured":"Yoshinori Aono , Takuya Hayashi , Lihua Wang , Shiho Moriai , 2017 . Privacy-preserving deep learning via additively homomorphic encryption . IEEE Transactions on Information Forensics and Security 13 , 5(2017), 1333 \u2013 1345 . Yoshinori Aono, Takuya Hayashi, Lihua Wang, Shiho Moriai, 2017. Privacy-preserving deep learning via additively homomorphic encryption. IEEE Transactions on Information Forensics and Security 13, 5(2017), 1333\u20131345.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2007.4286571"},{"key":"e_1_3_2_1_4_1","volume-title":"31st USENIX Security Symposium (USENIX Security 22)","author":"Fu Chong","year":"2022","unstructured":"Chong Fu , Xuhong Zhang , Shouling Ji , Jinyin Chen , Jingzheng Wu , Shanqing Guo , Jun Zhou , Alex\u00a0 X Liu , and Ting Wang . 2022 . Label inference attacks against vertical federated learning . In 31st USENIX Security Symposium (USENIX Security 22) , Boston, MA. Chong Fu, Xuhong Zhang, Shouling Ji, Jinyin Chen, Jingzheng Wu, Shanqing Guo, Jun Zhou, Alex\u00a0X Liu, and Ting Wang. 2022. Label inference attacks against vertical federated learning. In 31st USENIX Security Symposium (USENIX Security 22), Boston, MA."},{"key":"e_1_3_2_1_5_1","unstructured":"Robin\u00a0C Geyer Tassilo Klein and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557(2017).  Robin\u00a0C Geyer Tassilo Klein and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557(2017)."},{"key":"e_1_3_2_1_6_1","unstructured":"Stephen Hardy Wilko Henecka Hamish Ivey-Law Richard Nock Giorgio Patrini Guillaume Smith and Brian Thorne. 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677(2017).  Stephen Hardy Wilko Henecka Hamish Ivey-Law Richard Nock Giorgio Patrini Guillaume Smith and Brian Thorne. 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677(2017)."},{"key":"e_1_3_2_1_7_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2, 7","author":"Hinton Geoffrey","year":"2015","unstructured":"Geoffrey Hinton , Oriol Vinyals , Jeff Dean , 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2, 7 ( 2015 ). Geoffrey Hinton, Oriol Vinyals, Jeff Dean, 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 2, 7 (2015)."},{"key":"e_1_3_2_1_8_1","volume-title":"FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning. arXiv preprint arXiv:2109.00675(2021).","author":"Jiang Zhifeng","year":"2021","unstructured":"Zhifeng Jiang , Wei Wang , and Yang Liu . 2021 . FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning. arXiv preprint arXiv:2109.00675(2021). Zhifeng Jiang, Wei Wang, and Yang Liu. 2021. FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning. arXiv preprint arXiv:2109.00675(2021)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Peter Kairouz H\u00a0Brendan McMahan Brendan Avent Aur\u00e9lien Bellet Mehdi Bennis Arjun\u00a0Nitin Bhagoji Kallista Bonawitz Zachary Charles Graham Cormode Rachel Cummings 2021. Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning 14 1\u20132(2021) 1\u2013210.  Peter Kairouz H\u00a0Brendan McMahan Brendan Avent Aur\u00e9lien Bellet Mehdi Bennis Arjun\u00a0Nitin Bhagoji Kallista Bonawitz Zachary Charles Graham Cormode Rachel Cummings 2021. Advances and open problems in federated learning. Foundations and Trends\u00ae in Machine Learning 14 1\u20132(2021) 1\u2013210.","DOI":"10.1561\/2200000083"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP51992.2021.00029"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00023"},{"key":"e_1_3_2_1_12_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 (AISTATS\u201917). 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 (AISTATS\u201917). 1273\u20131282."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"e_1_3_2_1_14_1","unstructured":"Pavlo Molchanov Stephen Tyree Tero Karras Timo Aila and Jan Kautz. 2016. Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440(2016).  Pavlo Molchanov Stephen Tyree Tero Karras Timo Aila and Jan Kautz. 2016. Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440(2016)."},{"key":"e_1_3_2_1_15_1","volume-title":"Toward robustness and privacy in federated learning: Experimenting with local and central differential privacy. arXiv e-prints","author":"Naseri Mohammad","year":"2020","unstructured":"Mohammad Naseri , Jamie Hayes , and Emiliano De\u00a0Cristofaro . 2020. Toward robustness and privacy in federated learning: Experimenting with local and central differential privacy. arXiv e-prints ( 2020 ), arXiv\u20132009. Mohammad Naseri, Jamie Hayes, and Emiliano De\u00a0Cristofaro. 2020. Toward robustness and privacy in federated learning: Experimenting with local and central differential privacy. arXiv e-prints (2020), arXiv\u20132009."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT44484.2020.9174426"},{"key":"e_1_3_2_1_18_1","volume-title":"Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 1310\u20131321","author":"Shokri Reza","year":"2015","unstructured":"Reza Shokri and Vitaly Shmatikov . 2015 . Privacy-preserving deep learning . In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 1310\u20131321 . Reza Shokri and Vitaly Shmatikov. 2015. Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. 1310\u20131321."},{"key":"e_1_3_2_1_19_1","volume-title":"LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy. In the 30th International Joint Conference on Artificial Intelligence (IJCAI\u201921)","author":"Sun Lichao","year":"2021","unstructured":"Lichao Sun , Jianwei Qian , and Xun Chen . 2021 . LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy. In the 30th International Joint Conference on Artificial Intelligence (IJCAI\u201921) . Lichao Sun, Jianwei Qian, and Xun Chen. 2021. LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy. In the 30th International Joint Conference on Artificial Intelligence (IJCAI\u201921)."},{"key":"e_1_3_2_1_20_1","unstructured":"Cheng Tai Tong Xiao Yi Zhang Xiaogang Wang 2015. Convolutional neural networks with low-rank regularization. arXiv preprint arXiv:1511.06067(2015).  Cheng Tai Tong Xiao Yi Zhang Xiaogang Wang 2015. Convolutional neural networks with low-rank regularization. arXiv preprint arXiv:1511.06067(2015)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005465"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3378679.3394533"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01585-4"},{"key":"e_1_3_2_1_24_1","volume-title":"2020 USENIX Annual Technical Conference (USENIX ATC 20)","author":"Zhang Chengliang","year":"2020","unstructured":"Chengliang Zhang , Suyi Li , Junzhe Xia , Wei Wang , Feng Yan , and Yang Liu . 2020 . {BatchCrypt}: Efficient Homomorphic Encryption for {Cross-Silo} Federated Learning . In 2020 USENIX Annual Technical Conference (USENIX ATC 20) . 493\u2013506. Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu. 2020. {BatchCrypt}: Efficient Homomorphic Encryption for {Cross-Silo} Federated Learning. In 2020 USENIX Annual Technical Conference (USENIX ATC 20). 493\u2013506."},{"key":"e_1_3_2_1_25_1","unstructured":"Yue Zhao Meng Li Liangzhen Lai Naveen Suda Damon Civin and Vikas Chandra. 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582(2018).  Yue Zhao Meng Li Liangzhen Lai Naveen Suda Damon Civin and Vikas Chandra. 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582(2018)."},{"key":"e_1_3_2_1_26_1","volume-title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160(2016).","author":"Zhou Shuchang","year":"2016","unstructured":"Shuchang Zhou , Yuxin Wu , Zekun Ni , Xinyu Zhou , He Wen , and Yuheng Zou . 2016 . Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160(2016). Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou. 2016. Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv preprint arXiv:1606.06160(2016)."},{"key":"e_1_3_2_1_27_1","volume-title":"Deep leakage from gradients. Advances in Neural Information Processing Systems 32","author":"Zhu Ligeng","year":"2019","unstructured":"Ligeng Zhu , Zhijian Liu , and Song Han . 2019. Deep leakage from gradients. Advances in Neural Information Processing Systems 32 ( 2019 ). Ligeng Zhu, Zhijian Liu, and Song Han. 2019. Deep leakage from gradients. Advances in Neural Information Processing Systems 32 (2019)."}],"event":{"name":"ACSAC: Annual Computer Security Applications Conference","location":"Austin TX USA","acronym":"ACSAC"},"container-title":["Proceedings of the 38th Annual Computer Security Applications Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3564625.3567973","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3564625.3567973","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:12Z","timestamp":1750183752000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3564625.3567973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,5]]},"references-count":27,"alternative-id":["10.1145\/3564625.3567973","10.1145\/3564625"],"URL":"https:\/\/doi.org\/10.1145\/3564625.3567973","relation":{},"subject":[],"published":{"date-parts":[[2022,12,5]]},"assertion":[{"value":"2022-12-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}