{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:04:14Z","timestamp":1766484254914,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"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":[[2023,5,9]]},"DOI":"10.1145\/3576842.3582328","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T22:58:08Z","timestamp":1682549888000},"page":"197-208","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["FedRule: Federated Rule Recommendation System with Graph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7045-0002","authenticated-orcid":false,"given":"Yuhang","family":"Yao","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, Carnegie Mellon University, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3930-4151","authenticated-orcid":false,"given":"Mohammad Mahdi","family":"Kamani","sequence":"additional","affiliation":[{"name":"Wyze Labs, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4173-0096","authenticated-orcid":false,"given":"Zhongwei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Wyze Labs, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6426-6682","authenticated-orcid":false,"given":"Lin","family":"Chen","sequence":"additional","affiliation":[{"name":"Wyze Labs, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0785-9291","authenticated-orcid":false,"given":"Carlee","family":"Joe-Wong","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Carnegie Mellon University, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1638-4963","authenticated-orcid":false,"given":"Tianqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Wyze Labs, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888","author":"Ammad-Ud-Din Muhammad","year":"2019","unstructured":"Muhammad Ammad-Ud-Din, Elena Ivannikova, Suleiman\u00a0A Khan, Were Oyomno, Qiang Fu, Kuan\u00a0Eeik Tan, and Adrian Flanagan. 2019. Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888 (2019)."},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of KDD cup and workshop, Vol.\u00a02007","author":"Bennett James","year":"2007","unstructured":"James Bennett, Stan Lanning, 2007. The netflix prize. In Proceedings of KDD cup and workshop, Vol.\u00a02007. New York, NY, USA., 35."},{"key":"e_1_3_2_1_3_1","volume-title":"Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263","author":"van\u00a0den Berg Rianne","year":"2017","unstructured":"Rianne van\u00a0den Berg, Thomas\u00a0N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.1016\/j.procs.2015.04.237"},{"key":"e_1_3_2_1_5_1","volume-title":"Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046","author":"Bonawitz Keith","year":"2019","unstructured":"Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Kone\u010dn\u1ef3, Stefano Mazzocchi, H\u00a0Brendan McMahan, 2019. Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 (2019)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1109\/MSP.2017.2693418"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_7_1","DOI":"10.1109\/MIS.2020.3014880"},{"key":"e_1_3_2_1_8_1","volume-title":"Fede: Embedding knowledge graphs in federated setting. arXiv preprint arXiv:2010.12882","author":"Chen Mingyang","year":"2020","unstructured":"Mingyang Chen, Wen Zhang, Zonggang Yuan, Yantao Jia, and Huajun Chen. 2020. Fede: Embedding knowledge graphs in federated setting. arXiv preprint arXiv:2010.12882 (2020)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1145\/3344211"},{"key":"e_1_3_2_1_10_1","volume-title":"Adaptive personalized federated learning. arXiv preprint arXiv:2003.13461","author":"Deng Yuyang","year":"2020","unstructured":"Yuyang Deng, Mohammad\u00a0Mahdi Kamani, and Mehrdad Mahdavi. 2020. Adaptive personalized federated learning. arXiv preprint arXiv:2003.13461 (2020)."},{"key":"e_1_3_2_1_11_1","first-page":"15111","article-title":"Distributionally robust federated averaging","volume":"33","author":"Deng Yuyang","year":"2020","unstructured":"Yuyang Deng, Mohammad\u00a0Mahdi Kamani, and Mehrdad Mahdavi. 2020. Distributionally robust federated averaging. Advances in Neural Information Processing Systems 33 (2020), 15111\u201315122.","journal-title":"Advances in Neural Information Processing Systems"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_12_1","DOI":"10.1145\/2020408.2020426"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1145\/2843948"},{"key":"e_1_3_2_1_14_1","volume-title":"Local sgd with periodic averaging: Tighter analysis and adaptive synchronization. Advances in Neural Information Processing Systems 32","author":"Haddadpour Farzin","year":"2019","unstructured":"Farzin Haddadpour, Mohammad\u00a0Mahdi Kamani, Mehrdad Mahdavi, and Viveck Cadambe. 2019. Local sgd with periodic averaging: Tighter analysis and adaptive synchronization. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 2350\u20132358","author":"Haddadpour Farzin","year":"2021","unstructured":"Farzin Haddadpour, Mohammad\u00a0Mahdi Kamani, Aryan Mokhtari, and Mehrdad Mahdavi. 2021. Federated learning with compression: Unified analysis and sharp guarantees. In International Conference on Artificial Intelligence and Statistics. PMLR, 2350\u20132358."},{"key":"e_1_3_2_1_16_1","volume-title":"On the convergence of local descent methods in federated learning. arXiv preprint arXiv:1910.14425","author":"Haddadpour Farzin","year":"2019","unstructured":"Farzin Haddadpour and Mehrdad Mahdavi. 2019. On the convergence of local descent methods in federated learning. arXiv preprint arXiv:1910.14425 (2019)."},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025\u20131035","author":"Hamilton L","year":"2017","unstructured":"William\u00a0L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025\u20131035."},{"key":"e_1_3_2_1_18_1","volume-title":"Fedgraphnn: A federated learning system and benchmark for graph neural networks. arXiv preprint arXiv:2104.07145","author":"He Chaoyang","year":"2021","unstructured":"Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr. 2021. Fedgraphnn: A federated learning system and benchmark for graph neural networks. arXiv preprint arXiv:2104.07145 (2021)."},{"key":"e_1_3_2_1_19_1","volume-title":"Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977","author":"Kairouz Peter","year":"2019","unstructured":"Peter Kairouz, H\u00a0Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun\u00a0Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, 2019. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019)."},{"key":"e_1_3_2_1_20_1","volume-title":"International Conference on Machine Learning. PMLR, 5132\u20135143","author":"Karimireddy Sai\u00a0Praneeth","year":"2020","unstructured":"Sai\u00a0Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda\u00a0Theertha Suresh. 2020. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning. PMLR, 5132\u20135143."},{"key":"e_1_3_2_1_21_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf N","year":"2016","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_22_1","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_1_23_1","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"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.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_1_24_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_25_1","volume-title":"com recommendations: Item-to-item collaborative filtering","author":"Linden Greg","year":"2003","unstructured":"Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing 7, 1 (2003), 76\u201380."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_26_1","DOI":"10.1145\/1772690.1772760"},{"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.","key":"e_1_3_2_1_27_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_28_1","DOI":"10.1145\/3131365.3131369"},{"key":"e_1_3_2_1_29_1","volume-title":"How to break anonymity of the netflix prize dataset. arXiv preprint cs\/0610105","author":"Narayanan Arvind","year":"2006","unstructured":"Arvind Narayanan and Vitaly Shmatikov. 2006. How to break anonymity of the netflix prize dataset. arXiv preprint cs\/0610105 (2006)."},{"key":"e_1_3_2_1_30_1","volume-title":"Federated Knowledge Graphs Embedding. arXiv preprint arXiv:2105.07615","author":"Peng Hao","year":"2021","unstructured":"Hao Peng, Haoran Li, Yangqiu Song, Vincent Zheng, and Jianxin Li. 2021. Federated Knowledge Graphs Embedding. arXiv preprint arXiv:2105.07615 (2021)."},{"key":"e_1_3_2_1_31_1","volume-title":"The graph neural network model","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli, Marco Gori, Ah\u00a0Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61\u201380."},{"key":"e_1_3_2_1_32_1","volume-title":"Ivan Titov, and Max Welling.","author":"Schlichtkrull Michael","year":"2018","unstructured":"Michael Schlichtkrull, Thomas\u00a0N Kipf, Peter Bloem, Rianne Van Den\u00a0Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In European semantic web conference. Springer, 593\u2013607."},{"key":"e_1_3_2_1_33_1","volume-title":"Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197","author":"Sun Zhiqing","year":"2019","unstructured":"Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_34_1","DOI":"10.1145\/2858036.2858556"},{"key":"e_1_3_2_1_35_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_36_1","DOI":"10.1007\/s10458-007-9021-x"},{"key":"e_1_3_2_1_37_1","volume-title":"Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925","author":"Wu Chuhan","year":"2021","unstructured":"Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, and Xing Xie. 2021. Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925 (2021)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_38_1","DOI":"10.1007\/978-3-030-64694-3_12"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_39_1","DOI":"10.1609\/aaai.v35i5.16590"},{"key":"e_1_3_2_1_40_1","volume-title":"Fedgcn: Convergence and communication tradeoffs in federated training of graph convolutional networks. arXiv preprint arXiv:2201.12433","author":"Yao Yuhang","year":"2022","unstructured":"Yuhang Yao and Carlee Joe-Wong. 2022. Fedgcn: Convergence and communication tradeoffs in federated training of graph convolutional networks. arXiv preprint arXiv:2201.12433 (2022)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_41_1","DOI":"10.1109\/IoTDI49375.2020.00017"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_42_1","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_1_43_1","first-page":"5165","article-title":"Link prediction based on graph neural networks","volume":"31","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. Advances in Neural Information Processing Systems 31 (2018), 5165\u20135175.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_44_1","volume-title":"Hierarchical graph pooling with structure learning. arXiv preprint arXiv:1911.05954","author":"Zhang Zhen","year":"2019","unstructured":"Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, and Can Wang. 2019. Hierarchical graph pooling with structure learning. arXiv preprint arXiv:1911.05954 (2019)."},{"key":"e_1_3_2_1_45_1","volume-title":"Federated learning with non-iid data. arXiv preprint arXiv:1806.00582","author":"Zhao Yue","year":"2018","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)."}],"event":{"sponsor":["SIGBED ACM Special Interest Group on Embedded Systems"],"acronym":"IoTDI '23","name":"IoTDI '23: International Conference on Internet-of-Things Design and Implementation","location":"San Antonio TX USA"},"container-title":["Proceedings of the 8th ACM\/IEEE Conference on Internet of Things Design and Implementation"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3576842.3582328","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3576842.3582328","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:08:58Z","timestamp":1750183738000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3576842.3582328"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,9]]},"references-count":45,"alternative-id":["10.1145\/3576842.3582328","10.1145\/3576842"],"URL":"https:\/\/doi.org\/10.1145\/3576842.3582328","relation":{},"subject":[],"published":{"date-parts":[[2023,5,9]]},"assertion":[{"value":"2023-05-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}