{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T01:10:55Z","timestamp":1777338655761,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Natural Science Foundation of China","award":["U22B2038, U23A20319, 62422202, 62172056, 62192784, 62272054"],"award-info":[{"award-number":["U22B2038, U23A20319, 62422202, 62172056, 62192784, 62272054"]}]},{"name":"The 8th Young Elite Scientists Sponsorship Program by CAST","award":["2022QNRC001"],"award-info":[{"award-number":["2022QNRC001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,28]]},"DOI":"10.1145\/3696410.3714722","type":"proceedings-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T22:52:18Z","timestamp":1745362338000},"page":"4582-4591","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Horizontal Federated Heterogeneous Graph Learning: A Multi-Scale Adaptive Solution to Data Distribution Challenges"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1570-4962","authenticated-orcid":false,"given":"Jia","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Haidian Qu, Beijing Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2662-3444","authenticated-orcid":false,"given":"Yawen","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Haidian Qu, Beijing Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6123-0043","authenticated-orcid":false,"given":"Zhe","family":"Xue","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Haidian Qu, Beijing Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8559-2628","authenticated-orcid":false,"given":"Yingxia","family":"Shao","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Haidian Qu, Beijing Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8822-0897","authenticated-orcid":false,"given":"Zeli","family":"Guan","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Haidian Qu, Beijing Shi, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9130-5736","authenticated-orcid":false,"given":"Wenling","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University, Haidian Qu, Beijing Shi, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama.","author":"Emre Acar Durmus Alp","year":"2021","unstructured":"Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N Whatmough, and Venkatesh Saligrama. 2021. Federated learning based on dynamic regularization. arXiv preprint arXiv:2111.04263 (2021)."},{"key":"e_1_3_2_1_2_1","volume-title":"Bias propagation in federated learning. arXiv preprint arXiv:2309.02160","author":"Chang Hongyan","year":"2023","unstructured":"Hongyan Chang and Reza Shokri. 2023. Bias propagation in federated learning. arXiv preprint arXiv:2309.02160 (2023)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00447"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25891"},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1797--1806","author":"Lee Wang-Chien","year":"2017","unstructured":"Tao-yang Fu, Wang-Chien Lee, and Zhen Lei. 2017. Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 1797--1806."},{"key":"e_1_3_2_1_6_1","volume-title":"FedHGN: a federated framework for heterogeneous graph neural networks. arXiv preprint arXiv:2305.09729","author":"Fu Xinyu","year":"2023","unstructured":"Xinyu Fu and Irwin King. 2023. FedHGN: a federated framework for heterogeneous graph neural networks. arXiv preprint arXiv:2305.09729 (2023)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00987"},{"key":"e_1_3_2_1_9_1","volume-title":"FedGR: Federated Learning with Gravitation Regulation for Double Imbalance Distribution. In International Conference on Database Systems for Advanced Applications. Springer, 703--718","author":"Guo Songyue","year":"2023","unstructured":"Songyue Guo, Xu Yang, Jiyuan Feng, Ye Ding, Wei Wang, Yunqing Feng, and Qing Liao. 2023. FedGR: Federated Learning with Gravitation Regulation for Double Imbalance Distribution. In International Conference on Database Systems for Advanced Applications. Springer, 703--718."},{"key":"e_1_3_2_1_10_1","volume-title":"ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML).","author":"He Chaoyang","year":"2021","unstructured":"Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, S Yu Philip, Yu Rong, et al. 2021. Fedgraphnn: A federated learning benchmark system for graph neural networks. In ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5833"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330970"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/math10061000"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_1_15_1","unstructured":"Cheng Yang Hongyi Zhang Yaoqi Liu Xiao Wang Chuan Shi Hui Han Tianyu Zhao. 2022. OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network. In CIKM."},{"key":"e_1_3_2_1_16_1","volume-title":"International conference on machine learning. PMLR, 5132--5143","author":"Karimireddy Sai Praneeth","year":"2020","unstructured":"Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. 2020. Scaffold: Stochastic controlled averaging for federated learning. In International conference on machine learning. PMLR, 5132--5143."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29258"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"e_1_3_2_1_20_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_1_21_1","volume-title":"On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189","author":"Li Xiang","year":"2019","unstructured":"Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2019. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189 (2019)."},{"key":"e_1_3_2_1_22_1","volume-title":"Ensemble distillation for robust model fusion in federated learning. Advances in neural information processing systems","author":"Lin Tao","year":"2020","unstructured":"Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi. 2020b. Ensemble distillation for robust model fusion in federated learning. Advances in neural information processing systems, Vol. 33 (2020), 2351--2363."},{"key":"e_1_3_2_1_23_1","volume-title":"Improving federated relational data modeling via basis alignment and weight penalty. arXiv preprint arXiv:2011.11369","author":"Lin Yilun","year":"2020","unstructured":"Yilun Lin, Chaochao Chen, Cen Chen, and Li Wang. 2020a. Improving federated relational data modeling via basis alignment and weight penalty. arXiv preprint arXiv:2011.11369 (2020)."},{"key":"e_1_3_2_1_24_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_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611966"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i5.28199"},{"key":"e_1_3_2_1_27_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_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.01.019"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i13.29365"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2932096"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-10989-8_14"},{"key":"e_1_3_2_1_32_1","volume-title":"ESWC 2018, Heraklion, Crete, Greece, June 3--7, 2018, proceedings 15","author":"Schlichtkrull Michael","year":"2018","unstructured":"Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In The semantic web: 15th international conference, ESWC 2018, Heraklion, Crete, Greece, June 3--7, 2018, proceedings 15. Springer, 593--607."},{"key":"e_1_3_2_1_33_1","volume-title":"Clip-guided federated learning on heterogeneous and long-tailed data. arXiv preprint arXiv:2312.08648","author":"Shi Jiangming","year":"2023","unstructured":"Jiangming Shi, Shanshan Zheng, Xiangbo Yin, Yang Lu, Yuan Xie, and Yanyun Qu. 2023. Clip-guided federated learning on heterogeneous and long-tailed data. arXiv preprint arXiv:2312.08648 (2023)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26187"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i21.30557"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26192"},{"key":"e_1_3_2_1_38_1","volume-title":"Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. 2019b. Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315 (2019)."},{"key":"e_1_3_2_1_39_1","first-page":"1581","article-title":"Heterogeneous information network embedding with adversarial disentangler","volume":"35","author":"Wang Ruijia","year":"2021","unstructured":"Ruijia Wang, Chuan Shi, Tianyu Zhao, Xiao Wang, and Yanfang Ye. 2021. Heterogeneous information network embedding with adversarial disentangler. IEEE Transactions on Knowledge and Data Engineering, Vol. 35, 2 (2021), 1581--1593.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Xiao Wang Houye Ji Chuan Shi Bai Wang Yanfang Ye Peng Cui and Philip S Yu. 2019a. Heterogeneous graph attention network. In The world wide web conference. 2022--2032.","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26237"},{"key":"e_1_3_2_1_42_1","volume-title":"Federated graph classification over non-iid graphs. Advances in neural information processing systems","author":"Xie Han","year":"2021","unstructured":"Han Xie, Jing Ma, Li Xiong, and Carl Yang. 2021. Federated graph classification over non-iid graphs. Advances in neural information processing systems, Vol. 34 (2021), 18839--18852."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583471"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645693"},{"key":"e_1_3_2_1_45_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Yang Zhiqin","year":"2024","unstructured":"Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, and Bo Han. 2024. FedFed: Feature distillation against data heterogeneity in federated learning. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_46_1","volume-title":"FedGCN: Convergence-communication tradeoffs in federated training of graph convolutional networks. Advances in neural information processing systems","author":"Yao Yuhang","year":"2024","unstructured":"Yuhang Yao, Weizhao Jin, Srivatsan Ravi, and Carlee Joe-Wong. 2024. FedGCN: Convergence-communication tradeoffs in federated training of graph convolutional networks. Advances in neural information processing systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_47_1","volume-title":"Graph transformer networks. Advances in neural information processing systems","author":"Yun Seongjun","year":"2019","unstructured":"Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. 2019. Graph transformer networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29617"},{"key":"e_1_3_2_1_50_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_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Jianan Zhao Xiao Wang Chuan Shi Zekuan Liu and Yanfang Ye. 2020b. Network schema preserving heterogeneous information network embedding. In International joint conference on artificial intelligence (IJCAI).","DOI":"10.24963\/ijcai.2020\/190"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380134"},{"key":"e_1_3_2_1_54_1","volume-title":"International conference on machine learning. PMLR, 12878--12889","author":"Zhu Zhuangdi","year":"2021","unstructured":"Zhuangdi Zhu, Junyuan Hong, and Jiayu Zhou. 2021. Data-free knowledge distillation for heterogeneous federated learning. In International conference on machine learning. PMLR, 12878--12889."}],"event":{"name":"WWW '25: The ACM Web Conference 2025","location":"Sydney NSW Australia","acronym":"WWW '25","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714722","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696410.3714722","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:57Z","timestamp":1750295937000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714722"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":54,"alternative-id":["10.1145\/3696410.3714722","10.1145\/3696410"],"URL":"https:\/\/doi.org\/10.1145\/3696410.3714722","relation":{},"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"2025-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}