{"type":"ScholarlyArticle","title":"Federated Graph Semantic and Structural Learning","authors":[{"name":"Wenke Huang","slug":"wenke-huang","given":"Wenke","family":"Huang","affiliation":"School of Computer Science , Wuhan University , Wuhan , China"},{"name":"Guancheng Wan","slug":"guancheng-wan","given":"Guancheng","family":"Wan","affiliation":"School of Computer Science , Wuhan University , Wuhan , China"},{"name":"Mang Ye","slug":"mang-ye","given":"Mang","family":"Ye","affiliation":"School of Computer Science , Wuhan University , Wuhan , China; Hubei Luojia Laboratory , Wuhan , China"},{"name":"Bo Du","slug":"bo-du","given":"Bo","family":"Du","affiliation":"School of Computer Science , Wuhan University , Wuhan , China; Hubei Luojia Laboratory , Wuhan , China"}],"abstract":"Federated graph learning collaboratively learns a global graph neural network with distributed graphs, where the non-independent and identically distributed property is one of the major challenge. Most relative arts focus on traditional distributed tasks like images and voices, incapable of the graph structures. This paper firstly reveals that local client distortion is brought by both node-level semantics and graph-level structure. First, for node-level semantic, we find that contrasting nodes from distinct classes is beneficial to provide a well-performing discrimination. We pull the local node towards the global node of the same class and push them away from the global node of different classes. Second, we postulate that a well-structural graph neural network possesses similarity for neighbors due to the inherent adjacency relationships. However, aligning each node with adjacent nodes hinders discrimination due to the potential class inconsistency. We transform the adjacency relationships into the similarity distribution and leverage the global model to distill the relation knowledge into the local model, which preserves the structural information and discriminability of the local model. Empirical results on three graph datasets manifest the superiority of the proposed method over counterparts.","doi":"10.24963/ijcai.2023/426","url":"https://ijcai.org/proceedings/2023/426","pdf":"https://ijcai.org/proceedings/2023/0426.pdf","markdown":"https://ijcai.org/proceedings/2023/426.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/426","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":426,"pages":{"first":3830,"last":3838},"keywords":["Machine Learning: ML: Sequence and graph learning","Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Baek et al., 2022] Jinheon Baek, Wonyong Jeong, Jiong- dao Jin, Jaehong Yoon, and Sung Ju Hwang. 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