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Syst."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    Most existing GNN-based recommendation methods focus on exploiting a user\u2013item heterogeneous graph, which, however, will cause efficiency and effectiveness challenges, in a federated learning setting considering user privacy. We find that a user\u2013user or item\u2013item homogeneous graph is often privacy-insensitive and can significantly enhance the efficiency and effectiveness of federated graph embedding learning. Hence, we propose a novel framework called\n                    <jats:bold>Federated Homogeneous Graph Neural Network (FedHoG)<\/jats:bold>\n                    , which can provide privacy-preserving recommendations with high-quality and communication-efficient graph learning. We first design a privacy-preserving homogeneous graph construction method, which enables the server to construct an item\u2013item graph and a user\u2013user graph without leaking user privacy. Then, we develop a federated homogeneous graph learning method that enables balanced GNN model training among the server and clients. We also propose a lightweight homogeneous graph convolution method to achieve better graph embedding learning. Finally, extensive experiments on three public datasets show the advantages of our FedHoG in performance and efficiency. The datasets, source codes, and scripts are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/XZHhong\/FedHoG\">https:\/\/github.com\/XZHhong\/FedHoG<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3787468","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T13:11:03Z","timestamp":1772457063000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["FedHoG: Federated Homogeneous Graph Neural Network for Privacy-Preserving Recommendation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0168-6792","authenticated-orcid":false,"given":"Zihong","family":"Xian","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0522-2654","authenticated-orcid":false,"given":"Enyue","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6326-9531","authenticated-orcid":false,"given":"Weike","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6933-5760","authenticated-orcid":false,"given":"Zhong","family":"Ming","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3214303"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.28950"},{"key":"e_1_3_2_4_2","unstructured":"Muhammad Ammad-Ud-Din Elena Ivannikova Suleiman A. 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