{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T01:04:39Z","timestamp":1774055079114,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62102110, 92370204"],"award-info":[{"award-number":["62102110, 92370204"]}]},{"name":"National Key Research and Development Program of China","award":["2023YFF0725004"],"award-info":[{"award-number":["2023YFF0725004"]}]},{"name":"Guangzhou Basic and Applied Basic Research Program","award":["2024A04J3279"],"award-info":[{"award-number":["2024A04J3279"]}]},{"name":"Education Bureau of Guangzhou Municipality"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671660","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"968-979","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1448-5566","authenticated-orcid":false,"given":"Zhuoning","family":"Guo","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou) & The Hong Kong University of Science and Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8520-4069","authenticated-orcid":false,"given":"Duanyi","family":"Yao","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5059-8360","authenticated-orcid":false,"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4271-1567","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou) & The Hong Kong University of Science and Technology, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Aaditya Kumar Singh, and Sunav Choudhary","author":"Arivazhagan Manoj Ghuhan","year":"2019","unstructured":"Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 2019. Federated learning with personalization layers. arXiv preprint arXiv:1912.00818 (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"International Conference on Machine Learning. PMLR, 1396--1415","author":"Baek Jinheon","year":"2023","unstructured":"Jinheon Baek, Wonyong Jeong, Jiongdao Jin, Jaehong Yoon, and Sung Ju Hwang. 2023. Personalized subgraph federated learning. In International Conference on Machine Learning. PMLR, 1396--1415."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1968.1054109"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3125565"},{"key":"e_1_3_2_2_5_1","volume-title":"Foundations and Trends\u00ae in Theoretical Computer Science","volume":"9","author":"Dwork Cynthia","year":"2014","unstructured":"Cynthia Dwork, Aaron Roth, et al. 2014. The algorithmic foundations of differential privacy. Foundations and Trends\u00ae in Theoretical Computer Science, Vol. 9, 3--4 (2014), 211--407."},{"key":"e_1_3_2_2_6_1","volume-title":"International conference on machine learning. PMLR, 1263--1272","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International conference on machine learning. PMLR, 1263--1272."},{"key":"e_1_3_2_2_7_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622491"},{"key":"e_1_3_2_2_9_1","volume-title":"Cross-silo federated learning: Challenges and opportunities. arXiv preprint arXiv:2206.12949","author":"Huang Chao","year":"2022","unstructured":"Chao Huang, Jianwei Huang, and Xin Liu. 2022. Cross-silo federated learning: Challenges and opportunities. arXiv preprint arXiv:2206.12949 (2022)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1137\/08073408X"},{"key":"e_1_3_2_2_12_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. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_13_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_14_1","first-page":"1159","article-title":"Federated Learning Over Coupled Graphs","volume":"34","author":"Lei Runze","year":"2023","unstructured":"Runze Lei, Pinghui Wang, Junzhou Zhao, Lin Lan, Jing Tao, Chao Deng, Junlan Feng, Xidian Wang, and Xiaohong Guan. 2023. Federated Learning Over Coupled Graphs. IEEE Transactions on Parallel and Distributed Systems, Vol. 34, 4 (2023), 1159--1172.","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"key":"e_1_3_2_2_15_1","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. Proceedings of Machine learning and systems, Vol. 2 (2020), 429--450.","journal-title":"Proceedings of Machine learning and systems"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331191"},{"key":"e_1_3_2_2_17_1","first-page":"19035","article-title":"Practical adversarial attacks on spatiotemporal traffic forecasting models","volume":"35","author":"Liu Fan","year":"2022","unstructured":"Fan Liu, Hao Liu, and Wenzhao Jiang. 2022. Practical adversarial attacks on spatiotemporal traffic forecasting models. Advances in Neural Information Processing Systems, Vol. 35 (2022), 19035--19047.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_18_1","volume-title":"Federated Graph Neural Networks: Overview, Techniques, and Challenges","author":"Liu Rui","year":"2024","unstructured":"Rui Liu, Pengwei Xing, Zichao Deng, Anran Li, Cuntai Guan, and Han Yu. 2024. Federated Graph Neural Networks: Overview, Techniques, and Challenges. IEEE Transactions on Neural Networks and Learning Systems (2024)."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/IWQOS52092.2021.9521331"},{"key":"e_1_3_2_2_20_1","first-page":"1","article-title":"Federated social recommendation with graph neural network","volume":"13","author":"Liu Zhiwei","year":"2022","unstructured":"Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, and Philip S Yu. 2022. Federated social recommendation with graph neural network. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 13, 4 (2022), 1--24.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401090"},{"key":"e_1_3_2_2_22_1","unstructured":"Brendan McMahan Eider Moore Daniel Ramage Seth Hampson and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR 1273--1282."},{"key":"e_1_3_2_2_23_1","volume-title":"Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. knowledge discovery and data mining","author":"Meng Chuizheng","year":"2021","unstructured":"Chuizheng Meng, Sirisha Rambhatla, and Yan Liu. 2021. Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. knowledge discovery and data mining (2021)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458864.3466628"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539308"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00149"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583337"},{"key":"e_1_3_2_2_28_1","volume-title":"Collective classification in network data. AI magazine","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine, Vol. 29, 3 (2008), 93--93."},{"key":"e_1_3_2_2_29_1","volume-title":"Yuji Watanabe, Pablo Loyola, Daniel Klyashtorny, Heiko Ludwig, and Kumar Bhaskaran.","author":"Suzumura Toyotaro","year":"2019","unstructured":"Toyotaro Suzumura, Yi Zhou, Natahalie Baracaldo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Yuji Watanabe, Pablo Loyola, Daniel Klyashtorny, Heiko Ludwig, and Kumar Bhaskaran. 2019. Towards Federated Graph Learning for Collaborative Financial Crimes Detection. Research Papers in Economics (2019)."},{"key":"e_1_3_2_2_30_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations.","author":"Velivckovic Petar","year":"2018","unstructured":"Petar Velivckovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00060"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539112"},{"key":"e_1_3_2_2_33_1","first-page":"1","article-title":"A federated graph neural network framework for privacy-preserving personalization","volume":"13","author":"Wu Chuhan","year":"2022","unstructured":"Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Tao Qi, Yongfeng Huang, and Xing Xie. 2022. A federated graph neural network framework for privacy-preserving personalization. Nature Communications, Vol. 13, 1 (2022), 1--10.","journal-title":"Nature Communications"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/3579075.3579081"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.14778\/3579075.3579081"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_2_2_37_1","volume-title":"The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1215--1225","author":"Yu Qian","year":"2019","unstructured":"Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, and Salman A Avestimehr. 2019. Lagrange coded computing: Optimal design for resiliency, security, and privacy. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1215--1225."},{"key":"e_1_3_2_2_38_1","volume-title":"Fedstn: Graph representation driven federated learning for edge computing enabled urban traffic flow prediction","author":"Yuan Xiaoming","year":"2022","unstructured":"Xiaoming Yuan, Jiahui Chen, Jiayu Yang, Ning Zhang, Tingting Yang, Tao Han, and Amir Taherkordi. 2022. Fedstn: Graph representation driven federated learning for edge computing enabled urban traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems (2022)."},{"key":"e_1_3_2_2_39_1","volume-title":"Ppsgcn: A privacy-preserving subgraph sampling based distributed gcn training method. arXiv preprint arXiv:2110.12906","author":"Zhang Binchi","year":"2021","unstructured":"Binchi Zhang, Minnan Luo, Shangbin Feng, Ziqi Liu, Jun Zhou, and Qinghua Zheng. 2021. Ppsgcn: A privacy-preserving subgraph sampling based distributed gcn training method. arXiv preprint arXiv:2110.12906 (2021)."},{"key":"e_1_3_2_2_40_1","first-page":"6671","article-title":"Subgraph federated learning with missing neighbor generation","volume":"34","author":"Zhang Ke","year":"2021","unstructured":"Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, and Siu Ming Yiu. 2021. Subgraph federated learning with missing neighbor generation. Advances in Neural Information Processing Systems, Vol. 34 (2021), 6671--6682.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467187"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3563219"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3595185","article-title":"Trading Off Privacy, Utility, and Efficiency in Federated Learning","volume":"14","author":"Zhang Xiaojin","year":"2023","unstructured":"Xiaojin Zhang, Yan Kang, Kai Chen, Lixin Fan, and Qiang Yang. 2023. Trading Off Privacy, Utility, and Efficiency in Federated Learning. ACM Transactions on Intelligent Systems and Technology, Vol. 14, 6 (2023), 1--32.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_2_2_44_1","volume-title":"Deep leakage from gradients. Advances in neural information processing systems","author":"Zhu Ligeng","year":"2019","unstructured":"Ligeng Zhu, Zhijian Liu, and Song Han. 2019. Deep leakage from gradients. Advances in neural information processing systems, Vol. 32 (2019)."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671660","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671660","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:00Z","timestamp":1750291560000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671660"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":44,"alternative-id":["10.1145\/3637528.3671660","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671660","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}