{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T20:24:58Z","timestamp":1775420698519,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,3,17]]},"DOI":"10.1145\/3590003.3590051","type":"proceedings-article","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T18:22:56Z","timestamp":1685384576000},"page":"277-282","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["An Adaptive Gradient Privacy-Preserving Algorithm for Federated XGBoost"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6243-8243","authenticated-orcid":false,"given":"Hongyi","family":"Cai","sequence":"first","affiliation":[{"name":"Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1575-8239","authenticated-orcid":false,"given":"Jianping","family":"Cai","sequence":"additional","affiliation":[{"name":"Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9583-6040","authenticated-orcid":false,"given":"Lan","family":"Sun","sequence":"additional","affiliation":[{"name":"Fuzhou University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,29]]},"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","volume-title":"Secure training of decision trees with continuous attributes. Cryptology ePrint Archive","author":"Abspoel Mark","year":"2020","unstructured":"Mark Abspoel , Daniel Escudero , and Nikolaj Volgushev . 2020. Secure training of decision trees with continuous attributes. Cryptology ePrint Archive ( 2020 ). Mark Abspoel, Daniel Escudero, and Nikolaj Volgushev. 2020. Secure training of decision trees with continuous attributes. Cryptology ePrint Archive (2020)."},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Machine Learning. PMLR, 344\u2013353","author":"Assran Mahmoud","year":"2019","unstructured":"Mahmoud Assran , Nicolas Loizou , Nicolas Ballas , and Mike Rabbat . 2019 . Stochastic gradient push for distributed deep learning . In International Conference on Machine Learning. PMLR, 344\u2013353 . Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, and Mike Rabbat. 2019. Stochastic gradient push for distributed deep learning. In International Conference on Machine Learning. PMLR, 344\u2013353."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2021.3082561"},{"key":"e_1_3_2_1_6_1","volume-title":"The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science 9, 3\u20134","author":"Dwork Cynthia","year":"2014","unstructured":"Cynthia Dwork , Aaron Roth , 2014. The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science 9, 3\u20134 ( 2014 ), 211\u2013407. Cynthia Dwork, Aaron Roth, 2014. The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science 9, 3\u20134 (2014), 211\u2013407."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482361"},{"key":"e_1_3_2_1_8_1","volume-title":"Greedy function approximation: a gradient boosting machine. Annals of statistics","author":"Friedman H","year":"2001","unstructured":"Jerome\u00a0 H Friedman . 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics ( 2001 ), 1189\u20131232. Jerome\u00a0H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189\u20131232."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457241"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v38i3.2741"},{"key":"e_1_3_2_1_11_1","volume-title":"Dp-xgboost: Private machine learning at scale. arXiv preprint arXiv:2110.12770","author":"Grislain Nicolas","year":"2021","unstructured":"Nicolas Grislain and Joan Gonzalvez . 2021 . Dp-xgboost: Private machine learning at scale. arXiv preprint arXiv:2110.12770 (2021). Nicolas Grislain and Joan Gonzalvez. 2021. Dp-xgboost: Private machine learning at scale. arXiv preprint arXiv:2110.12770 (2021)."},{"key":"e_1_3_2_1_12_1","volume-title":"Fedxgboost: Privacy-preserving xgboost for federated learning. arXiv preprint arXiv:2106.10662","author":"Le Nhan\u00a0Khanh","year":"2021","unstructured":"Nhan\u00a0Khanh Le , Yang Liu , Quang\u00a0Minh Nguyen , Qingchen Liu , Fangzhou Liu , Quanwei Cai , and Sandra Hirche . 2021 . Fedxgboost: Privacy-preserving xgboost for federated learning. arXiv preprint arXiv:2106.10662 (2021). Nhan\u00a0Khanh Le, Yang Liu, Quang\u00a0Minh Nguyen, Qingchen Liu, Fangzhou Liu, Quanwei Cai, and Sandra Hirche. 2021. Fedxgboost: Privacy-preserving xgboost for federated learning. arXiv preprint arXiv:2106.10662 (2021)."},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of Machine learning and systems 2","author":"Li Tian","year":"2020","unstructured":"Tian Li , Anit\u00a0Kumar Sahu , Manzil Zaheer , Maziar Sanjabi , Ameet Talwalkar , and Virginia Smith . 2020 . Federated optimization in heterogeneous networks . Proceedings of Machine learning and systems 2 (2020), 429\u2013450. Tian Li, Anit\u00a0Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems 2 (2020), 429\u2013450."},{"key":"e_1_3_2_1_14_1","volume-title":"OpBoost: a vertical federated tree boosting framework based on order-preserving desensitization. arXiv preprint arXiv:2210.01318","author":"Li Xiaochen","year":"2022","unstructured":"Xiaochen Li , Yuke Hu , Weiran Liu , Hanwen Feng , Li Peng , Yuan Hong , Kui Ren , and Zhan Qin . 2022. OpBoost: a vertical federated tree boosting framework based on order-preserving desensitization. arXiv preprint arXiv:2210.01318 ( 2022 ). Xiaochen Li, Yuke Hu, Weiran Liu, Hanwen Feng, Li Peng, Yuan Hong, Kui Ren, and Zhan Qin. 2022. OpBoost: a vertical federated tree boosting framework based on order-preserving desensitization. arXiv preprint arXiv:2210.01318 (2022)."},{"key":"e_1_3_2_1_15_1","volume-title":"Threats to federated learning: A survey. arXiv preprint arXiv:2003.02133","author":"Lyu Lingjuan","year":"2020","unstructured":"Lingjuan Lyu , Han Yu , and Qiang Yang . 2020. Threats to federated learning: A survey. arXiv preprint arXiv:2003.02133 ( 2020 ). Lingjuan Lyu, Han Yu, and Qiang Yang. 2020. Threats to federated learning: A survey. arXiv preprint arXiv:2003.02133 (2020)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560687"},{"key":"e_1_3_2_1_17_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_18_1","volume-title":"R\u00e9nyi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF)","author":"Mironov Ilya","unstructured":"Ilya Mironov . 2017. R\u00e9nyi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF) . IEEE , 263\u2013275. Ilya Mironov. 2017. R\u00e9nyi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF). IEEE, 263\u2013275."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2019.00021"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488502001648"},{"key":"e_1_3_2_1_21_1","volume-title":"Federboost: Private federated learning for gbdt. arXiv preprint arXiv:2011.02796","author":"Tian Zhihua","year":"2020","unstructured":"Zhihua Tian , Rui Zhang , Xiaoyang Hou , Jian Liu , and Kui Ren . 2020 . Federboost: Private federated learning for gbdt. arXiv preprint arXiv:2011.02796 (2020). Zhihua Tian, Rui Zhang, Xiaoyang Hou, Jian Liu, and Kui Ren. 2020. Federboost: Private federated learning for gbdt. arXiv preprint arXiv:2011.02796 (2020)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486352"}],"event":{"name":"CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","location":"Shanghai China","acronym":"CACML 2023"},"container-title":["Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3590003.3590051","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3590003.3590051","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:16Z","timestamp":1750183756000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3590003.3590051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,17]]},"references-count":23,"alternative-id":["10.1145\/3590003.3590051","10.1145\/3590003"],"URL":"https:\/\/doi.org\/10.1145\/3590003.3590051","relation":{},"subject":[],"published":{"date-parts":[[2023,3,17]]},"assertion":[{"value":"2023-05-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}