{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:01:38Z","timestamp":1773842498910,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":72,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hong Kong RGC","award":["TRS T41-603\/20-R"],"award-info":[{"award-number":["TRS T41-603\/20-R"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,4,22]]},"DOI":"10.1145\/3627703.3629563","type":"proceedings-article","created":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T06:28:28Z","timestamp":1713421708000},"page":"489-504","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Accelerating Privacy-Preserving Machine Learning With GeniBatch"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4830-7808","authenticated-orcid":false,"given":"Xinyang","family":"Huang","sequence":"first","affiliation":[{"name":"iSING Lab, Hong Kong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6926-7801","authenticated-orcid":false,"given":"Junxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"iSING Lab, Hong Kong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3696-064X","authenticated-orcid":false,"given":"Xiaodian","family":"Cheng","sequence":"additional","affiliation":[{"name":"iSING Lab, Hong Kong University of Science and Technology and University of Waterloo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7468-3746","authenticated-orcid":false,"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Waterloo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9502-7622","authenticated-orcid":false,"given":"Yilun","family":"Jin","sequence":"additional","affiliation":[{"name":"iSING Lab, Hong Kong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3620-4843","authenticated-orcid":false,"given":"Shuihai","family":"Hu","sequence":"additional","affiliation":[{"name":"iSING Lab, Hong Kong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3238-8500","authenticated-orcid":false,"given":"Han","family":"Tian","sequence":"additional","affiliation":[{"name":"iSING Lab, Hong Kong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2587-6028","authenticated-orcid":false,"given":"Kai","family":"Chen","sequence":"additional","affiliation":[{"name":"iSING Lab, Hong Kong University of Science and Technology and USTC"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"https:\/\/fate.fedai.org\/","author":"Fate","year":"2019","unstructured":"Fate (federated ai technology enabler). https:\/\/fate.fedai.org\/, 2019."},{"key":"e_1_3_2_1_2_1","volume-title":"https:\/\/github.com\/bytedance\/fedlearner","year":"2019","unstructured":"Fedlearner. https:\/\/github.com\/bytedance\/fedlearner, 2019."},{"key":"e_1_3_2_1_3_1","volume-title":"https:\/\/github.com\/tf-encrypted\/tf-encrypted","author":"Tf","year":"2019","unstructured":"Tf encrypted. https:\/\/github.com\/tf-encrypted\/tf-encrypted, 2019."},{"key":"e_1_3_2_1_4_1","volume-title":"https:\/\/github.com\/FederatedAI\/FATE\/tree\/master\/python\/federatedml","author":"Federatedml","year":"2019","unstructured":"Federatedml (federated machine learning). https:\/\/github.com\/FederatedAI\/FATE\/tree\/master\/python\/federatedml, 2019."},{"key":"e_1_3_2_1_5_1","volume-title":"https:\/\/github.com\/Microsoft\/SEAL","author":"Microsoft","year":"2022","unstructured":"Microsoft seal. https:\/\/github.com\/Microsoft\/SEAL, 2022."},{"key":"e_1_3_2_1_6_1","volume-title":"https:\/\/oag.ca.gov\/privacy\/ccpa","author":"California","year":"2018","unstructured":"California consumer privacy act (ccpa). https:\/\/oag.ca.gov\/privacy\/ccpa, 2018."},{"key":"e_1_3_2_1_7_1","volume-title":"https:\/\/man7.org\/linux\/man-pages\/man8\/tc.8.html","year":"2022","unstructured":"tc(8)-linux manual page. https:\/\/man7.org\/linux\/man-pages\/man8\/tc.8.html, 2022."},{"key":"e_1_3_2_1_8_1","volume-title":"CCS","author":"Abadi Martin","year":"2016","unstructured":"Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In CCS, 2016."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSEC.2018.2888775"},{"key":"e_1_3_2_1_11_1","volume-title":"Qsgd: Communication-efficient sgd via gradient quantization and encoding. Advances in neural information processing systems, 30","author":"Alistarh Dan","year":"2017","unstructured":"Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. Qsgd: Communication-efficient sgd via gradient quantization and encoding. Advances in neural information processing systems, 30, 2017."},{"issue":"5","key":"e_1_3_2_1_12_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, et al. Privacy-preserving deep learning via additively homomorphic encryption. IEEE Transactions on Information Forensics and Security, 13(5):1333--1345, 2017.","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"e_1_3_2_1_13_1","first-page":"15453","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Bagdasaryan Eugene","year":"2019","unstructured":"Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov. Differential privacy has disparate impact on model accuracy. In Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch\u00e9-Buc, Emily B. Fox, and Roman Garnett, editors, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 15453--15462, 2019."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5308"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467210"},{"key":"e_1_3_2_1_16_1","volume-title":"Machine learning and prediction in medicine---beyond the peak of inflated expectations. The New England journal of medicine, 376(26):2507","author":"Chen Jonathan H","year":"2017","unstructured":"Jonathan H Chen and Steven M Asch. Machine learning and prediction in medicine---beyond the peak of inflated expectations. The New England journal of medicine, 376(26):2507, 2017."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2021.3082561"},{"key":"e_1_3_2_1_18_1","volume-title":"Haflo: Gpu-based acceleration for federated logistic regression. arXiv preprint arXiv:2107.13797","author":"Cheng Xiaodian","year":"2021","unstructured":"Xiaodian Cheng, Wanhang Lu, Xinyang Huang, Shuihai Hu, and Kai Chen. Haflo: Gpu-based acceleration for federated logistic regression. arXiv preprint arXiv:2107.13797, 2021."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387107"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70694-8_15"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2015.23113"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3470449"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3027971"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Kyle Fritchman Keerthanaa Saminathan Rafael Dowsley Tyler Hughes Martine De Cock Anderson C. A. Nascimento and Ankur Teredesai. Privacy-preserving scoring of tree ensembles: A novel framework for AI in healthcare. In Naoki Abe Huan Liu Calton Pu Xiaohua Hu Nesreen K. Ahmed Mu Qiao Yang Song Donald Kossmann Bing Liu Kisung Lee Jiliang Tang Jingrui He and Jeffrey S. Saltz editors IEEE International Conference on Big Data (IEEE BigData 2018) Seattle WA USA December 10-13 2018 pages 2413--2422. IEEE 2018.","DOI":"10.1109\/BigData.2018.8622627"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457241"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.2501\/IJMR-2017-050"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-28632-5_9"},{"key":"e_1_3_2_1_28_1","volume-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677","author":"Hardy Stephen","year":"2017","unstructured":"Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 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_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_30_1","first-page":"629","volume-title":"14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17)","author":"Hsieh Kevin","year":"2017","unstructured":"Kevin Hsieh, Aaron Harlap, Nandita Vijaykumar, Dimitris Konomis, Gregory R Ganger, Phillip B Gibbons, and Onur Mutlu. Gaia:{Geo-Distributed} machine learning approaching {LAN} speeds. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17), pages 629--647, 2017."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_32_1","first-page":"1651","volume-title":"27th USENIX Security Symposium (USENIX Security 18)","author":"Juvekar Chiraag","year":"2018","unstructured":"Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan. { GAZELLE}: A low latency framework for secure neural network inference. In 27th USENIX Security Symposium (USENIX Security 18), pages 1651--1669, 2018."},{"key":"e_1_3_2_1_33_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014."},{"key":"e_1_3_2_1_34_1","volume-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky Alex","year":"2009","unstructured":"Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009."},{"key":"e_1_3_2_1_35_1","first-page":"19","volume-title":"15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21)","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V Madhyastha, and Mosharaf Chowdhury. Oort: Efficient federated learning via guided participant selection. In 15th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 21), pages 19--35, 2021."},{"key":"e_1_3_2_1_36_1","first-page":"741","volume-title":"18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21)","author":"Lao ChonLam","year":"2021","unstructured":"ChonLam Lao, Yanfang Le, Kshiteej Mahajan, Yixi Chen, Wenfei Wu, Aditya Akella, and Michael Swift. {ATP}: In-network aggregation for multi-tenant learning. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21), pages 741--761, 2021."},{"key":"e_1_3_2_1_37_1","volume-title":"Asynchronous parallel stochastic gradient for nonconvex optimization. Advances in neural information processing systems, 28","author":"Lian Xiangru","year":"2015","unstructured":"Xiangru Lian, Yijun Huang, Yuncheng Li, and Ji Liu. Asynchronous parallel stochastic gradient for nonconvex optimization. Advances in neural information processing systems, 28, 2015."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-17277-0_9"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-17277-0_9"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134056"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1109\/ICDCS.2019.00099","volume-title":"2019 IEEE 39th international conference on distributed computing systems (ICDCS)","author":"Luping WANG","year":"2019","unstructured":"WANG Luping, WANG Wei, and LI Bo. Cmfl: Mitigating communication overhead for federated learning. In 2019 IEEE 39th international conference on distributed computing systems (ICDCS), pages 954--964. IEEE, 2019."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796752"},{"key":"e_1_3_2_1_43_1","volume-title":"PMLR","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273--1282. PMLR, 2017."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1515\/phys-2017-0014"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243760"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2013.30"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756123.1756146"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359642"},{"key":"e_1_3_2_1_49_1","volume-title":"How to exchange secrets with oblivious transfer. Cryptology ePrint Archive","author":"Rabin Michael O","year":"2005","unstructured":"Michael O Rabin. How to exchange secrets with oblivious transfer. Cryptology ePrint Archive, 2005."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228872"},{"key":"e_1_3_2_1_51_1","first-page":"8253","volume-title":"International Conference on Machine Learning","author":"Rothchild Daniel","year":"2020","unstructured":"Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora. Fetchsgd: Communication-efficient federated learning with sketching. In International Conference on Machine Learning, pages 8253--8265. PMLR, 2020."},{"key":"e_1_3_2_1_52_1","first-page":"6105","volume-title":"International conference on machine learning","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105--6114. PMLR, 2019."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833648"},{"key":"e_1_3_2_1_54_1","volume-title":"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, 4(2):26--31","author":"Tieleman Tijmen","year":"2012","unstructured":"Tijmen Tieleman, Geoffrey Hinton, et al. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, 4(2):26--31, 2012."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.2478\/popets-2019-0035"},{"key":"e_1_3_2_1_56_1","volume-title":"Falcon: Honest-majority maliciously secure framework for private deep learning. arXiv preprint arXiv:2004.02229","author":"Wagh Sameer","year":"2020","unstructured":"Sameer Wagh, Shruti Tople, Fabrice Benhamouda, Eyal Kushilevitz, Prateek Mittal, and Tal Rabin. Falcon: Honest-majority maliciously secure framework for private deep learning. arXiv preprint arXiv:2004.02229, 2020."},{"key":"e_1_3_2_1_57_1","unstructured":"Hao Wang Yuxuan Qin ChonLam Lao Yanfang Le Wenfei Wu and Kai Chen. Preemptive switch memory usage to accelerate training jobs with shared in-network aggregation."},{"key":"e_1_3_2_1_58_1","first-page":"31","article-title":"Gradient sparsification for communication-efficient distributed optimization","author":"Wangni Jianqiao","year":"2018","unstructured":"Jianqiao Wangni, Jialei Wang, Ji Liu, and Tong Zhang. Gradient sparsification for communication-efficient distributed optimization. Advances in Neural Information Processing Systems, 31, 2018.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_59_1","volume-title":"Terngrad: Ternary gradients to reduce communication in distributed deep learning. Advances in neural information processing systems, 30","author":"Wen Wei","year":"2017","unstructured":"Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li. Terngrad: Ternary gradients to reduce communication in distributed deep learning. Advances in neural information processing systems, 30, 2017."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343180.3343186"},{"key":"e_1_3_2_1_61_1","volume-title":"Tacc: A full-stack cloud computing infrastructure for machine learning tasks. arXiv preprint arXiv:2110.01556","author":"Xu Kaiqiang","year":"2021","unstructured":"Kaiqiang Xu, Xinchen Wan, Hao Wang, Zhenghang Ren, Xudong Liao, Decang Sun, Chaoliang Zeng, and Kai Chen. Tacc: A full-stack cloud computing infrastructure for machine learning tasks. arXiv preprint arXiv:2110.01556, 2021."},{"key":"e_1_3_2_1_62_1","volume-title":"Privacy-preserving machine learning: Methods, challenges and directions. CoRR, abs\/2108.04417","author":"Xu Runhua","year":"2021","unstructured":"Runhua Xu, Nathalie Baracaldo, and James Joshi. Privacy-preserving machine learning: Methods, challenges and directions. CoRR, abs\/2108.04417, 2021."},{"key":"e_1_3_2_1_63_1","volume-title":"Efficient batch homomorphic encryption for vertically federated xgboost. arXiv preprint arXiv:2112.04261","author":"Xu Wuxing","year":"2021","unstructured":"Wuxing Xu, Hao Fan, Kaixin Li, and Kai Yang. Efficient batch homomorphic encryption for vertically federated xgboost. arXiv preprint arXiv:2112.04261, 2021."},{"key":"e_1_3_2_1_64_1","volume-title":"A survey on vertical federated learning: From a layered perspective. arXiv preprint arXiv:2304.01829","author":"Yang Liu","year":"2023","unstructured":"Liu Yang, Di Chai, Junxue Zhang, Yilun Jin, Leye Wang, Hao Liu, Han Tian, Qian Xu, and Kai Chen. A survey on vertical federated learning: From a layered perspective. arXiv preprint arXiv:2304.01829, 2023."},{"key":"e_1_3_2_1_65_1","volume-title":"Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1--19","author":"Yang Qiang","year":"2019","unstructured":"Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2):1--19, 2019."},{"key":"e_1_3_2_1_66_1","volume-title":"Fpga-based hardware accelerator of homomorphic encryption for efficient federated learning. arXiv preprint arXiv:2007.10560","author":"Yang Zhaoxiong","year":"2020","unstructured":"Zhaoxiong Yang, Shuihai Hu, and Kai Chen. Fpga-based hardware accelerator of homomorphic encryption for efficient federated learning. arXiv preprint arXiv:2007.10560, 2020."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2019.8888042"},{"key":"e_1_3_2_1_68_1","first-page":"493","volume-title":"Efficient homomorphic encryption for {Cross-Silo} federated learning. In 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. {BatchCrypt}: Efficient homomorphic encryption for {Cross-Silo} federated learning. In 2020 USENIX annual technical conference (USENIX ATC 20), pages 493--506, 2020."},{"key":"e_1_3_2_1_69_1","first-page":"1057","volume-title":"20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)","author":"Zhang Junxue","year":"2023","unstructured":"Junxue Zhang, Xiaodian Cheng, Wei Wang, Liu Yang, Jinbin Hu, and Kai Chen. {FLASH}: Towards a high-performance hardware acceleration architecture for cross-silo federated learning. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23), pages 1057--1079, 2023."},{"key":"e_1_3_2_1_70_1","volume-title":"Sok: Fully homomorphic encryption accelerators. arXiv preprint arXiv:2212.01713","author":"Zhang Junxue","year":"2022","unstructured":"Junxue Zhang, Xiaodian Cheng, Liu Yang, Jinbin Hu, Ximeng Liu, and Kai Chen. Sok: Fully homomorphic encryption accelerators. arXiv preprint arXiv:2212.01713, 2022."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411495.3421352"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098029"}],"event":{"name":"EuroSys '24: Nineteenth European Conference on Computer Systems","location":"Athens Greece","acronym":"EuroSys '24","sponsor":["SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the Nineteenth European Conference on Computer Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627703.3629563","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627703.3629563","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T01:15:00Z","timestamp":1755825300000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627703.3629563"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,22]]},"references-count":72,"alternative-id":["10.1145\/3627703.3629563","10.1145\/3627703"],"URL":"https:\/\/doi.org\/10.1145\/3627703.3629563","relation":{},"subject":[],"published":{"date-parts":[[2024,4,22]]},"assertion":[{"value":"2024-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}