{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:33:10Z","timestamp":1763202790173},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Graph neural networks (GNNs) have been applied successfully in many machine learning tasks due to their advantages in utilizing neighboring information. Recently, with the global enactment of privacy protection regulations, federated GNNs have gained increasing attention in academia and industry. However, the graphs owned by different participants could be non-independently-and-identically distributed (non-IID), leading to the deterioration of federated GNNs' accuracy. In this paper, we propose a globally consistent federated graph autoencoder (GCFGAE) to overcome the non-IID problem in unsupervised federated graph learning via three innovations. First, by integrating federated learning with split learning, we train a unique global model instead of FedAvg-styled global and local models, yielding results consistent with that of the centralized GAE. Second, we design a collaborative computation mechanism considering overlapping vertices to reduce communication overhead during forward propagation. Third, we develop a layer-wise and block-wise gradient computation strategy to reduce the space and communication complexity during backward propagation. Experiments on real-world datasets demonstrate that GCFGAE achieves not only higher accuracy but also around 500 times lower communication overhead and 1000 times smaller space overhead than existing federated GNN models.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/419","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"3768-3776","source":"Crossref","is-referenced-by-count":4,"title":["Globally Consistent Federated Graph Autoencoder for Non-IID Graphs"],"prefix":"10.24963","author":[{"given":"Kun","family":"Guo","sequence":"first","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University"}]},{"given":"Yutong","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University"}]},{"given":"Qingqing","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University"}]},{"given":"Yuting","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University"}]},{"given":"Ziyao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University"}]},{"given":"Wenyu","family":"He","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University"}]},{"given":"Liu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and"},{"name":"Technology"}]},{"given":"Kai","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and"},{"name":"Technology"}]},{"given":"Ximeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University"}]},{"given":"Wenzhong","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer and Data Science, Fuzhou University"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:48:10Z","timestamp":1691743690000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/419"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/419","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}