{"type":"ScholarlyArticle","title":"FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks","authors":[{"name":"Xinyu Fu","slug":"xinyu-fu","given":"Xinyu","family":"Fu","affiliation":"The Chinese University of Hong Kong , Hong Kong , China"},{"name":"Irwin King","slug":"irwin-king","given":"Irwin","family":"King","affiliation":"The Chinese University of Hong Kong , Hong Kong , China"}],"abstract":"Heterogeneous graph neural networks (HGNNs) can learn from typed and relational graph data more effectively than conventional GNNs. With larger parameter spaces, HGNNs may require more training data, which is often scarce in real-world applications due to privacy regulations (e.g., GDPR). Federated graph learning (FGL) enables multiple clients to train a GNN collaboratively without sharing their local data. However, existing FGL methods mainly focus on homogeneous GNNs or knowledge graph embeddings; few have considered heterogeneous graphs and HGNNs. In federated heterogeneous graph learning, clients may have private graph schemas. Conventional FL/FGL methods attempting to define a global HGNN model would violate schema privacy. To address these challenges, we propose FedHGN, a novel and general FGL framework for HGNNs. FedHGN adopts schema-weight decoupling to enable schema-agnostic knowledge sharing and employs coefficients alignment to stabilize the training process and improve HGNN performance. With better privacy preservation, FedHGN consistently outperforms local training and conventional FL methods on three widely adopted heterogeneous graph datasets with varying client numbers. The code is available at https://github.com/cynricfu/FedHGN.","doi":"10.24963/ijcai.2023/412","url":"https://ijcai.org/proceedings/2023/412","pdf":"https://ijcai.org/proceedings/2023/0412.pdf","markdown":"https://ijcai.org/proceedings/2023/412.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/412","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":412,"pages":{"first":3705,"last":3713},"keywords":["Machine Learning: ML: Sequence and graph learning","Data Mining: DM: Mining graphs","Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Abadi et al., 2016] Martín Abadi, Andy Chu, Ian J. 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