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However, FL for subgraph models faces significant challenges, such as the diversity of data and the risk of attacks, which can affect the strength and reliability of these models. In response to these challenges, our research delves into the complexities of FL for subgraphs from an information theory perspective. We identify a major issue that affects the performance of graph models: the bias in the optimization goal of the commonly used FedAVG training method. To address this, we propose InfoFedGNN, an innovative FL framework for subgraphs that is based on the Information Bottleneck principle. InfoFedGNN is designed to overcome the problem of Non-Independent and Identically Distributed (non-i.i.d.) data in FL and to significantly improve its defense against security threats. Our thorough evaluation of InfoFedGNN on five public datasets, with both uniform and diverse data distributions, highlights its improved defense capabilities and better training outcomes. These results confirm the effectiveness of InfoFedGNN in enhancing the security and efficiency of FL, demonstrating its potential to push forward the development of federated graph models.<\/jats:p>","DOI":"10.1145\/3737879","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T11:41:36Z","timestamp":1748605296000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Subgraph Federated Learning with Information Bottleneck Constrained Generative Learning"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9496-1229","authenticated-orcid":false,"given":"Shangyang","family":"Li","sequence":"first","affiliation":[{"name":"Guangdong Institute of Intelligence Science and Technology, Zhuhai, China and Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7741-1153","authenticated-orcid":false,"given":"Jiayan","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Intelligence Science and Technology, Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2784440"},{"key":"e_1_3_1_3_2","unstructured":"Alexander A. 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