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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:p>As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, graph data are stored separately in multiple distributed parties in some practical scenarios, which may not be directly shared due to conflicts of interest. Hence, federated graph neural networks are proposed to address such data silo issues while preserving each party\u2019s privacy (or client). Nevertheless, different graph data distributions of various parties, which is known as the statistical heterogeneity, may degrade the performance of naive federated learning algorithms like FedAvg. In this article, we propose FedEgo, a federated graph learning framework based on ego-graphs to tackle the challenges above, in which each client will train their local models while also contributing to the training of a global model. FedEgo applies GraphSAGE over ego-graphs to make full use of the structure information and utilizes Mixup for privacy concerns. To deal with the statistical heterogeneity, we integrate personalization into learning and propose an adaptive mixing coefficient strategy that enables clients to achieve their optimal personalization. Extensive experimental results and in-depth analysis demonstrate the effectiveness of FedEgo.<\/jats:p>","DOI":"10.1145\/3624017","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T11:26:50Z","timestamp":1695209210000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2441-2861","authenticated-orcid":false,"given":"Taolin","family":"Zhang","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7689-0394","authenticated-orcid":false,"given":"Chengyuan","family":"Mai","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8149-0173","authenticated-orcid":false,"given":"Yaomin","family":"Chang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7048-3445","authenticated-orcid":false,"given":"Chuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7468-8766","authenticated-orcid":false,"given":"Lin","family":"Shu","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"Federated learning with personalization layers","author":"Arivazhagan Manoj Ghuhan","year":"2019","unstructured":"Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 2019. 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Improving federated learning personalization via model agnostic meta learning. arXiv preprint arXiv:1909.12488 (2019).","journal-title":"arXiv preprint arXiv:1909.12488"},{"key":"e_1_3_3_15_2","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2 (2020), 429\u2013450.","journal-title":"Proc. Mach. Learn. Syst."},{"key":"e_1_3_3_16_2","article-title":"Three approaches for personalization with applications to federated learning","author":"Mansour Yishay","year":"2020","unstructured":"Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh. 2020. 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