{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T22:17:56Z","timestamp":1677017876464},"reference-count":143,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2022,11,29]]},"abstract":"Graph machine learning has gained great attention in both academia and industry recently. Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are trained over massive graph data. However, in many realworld scenarios, such as hospitalization prediction in healthcare systems, the graph data is usually stored at multiple data owners and cannot be directly accessed by any other parties due to privacy concerns and regulation restrictions. Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner. In this survey, we conduct a comprehensive review of the literature in FGML. Specifically, we first provide a new taxonomy to divide the existing problems in FGML into two settings, namely, FL with structured data and structured FL. Then, we review the mainstream techniques in each setting and elaborate on how they address the challenges under FGML. In addition, we summarize the real-world applications of FGML from different domains and introduce open graph datasets and platforms adopted in FGML. Finally, we present several limitations in the existing studies with promising research directions in this field.<\/jats:p>","DOI":"10.1145\/3575637.3575644","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T14:14:48Z","timestamp":1670508888000},"page":"32-47","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Federated Graph Machine Learning"],"prefix":"10.1145","volume":"24","author":[{"given":"Xingbo","family":"Fu","sequence":"first","affiliation":[{"name":"University of Virginia"}]},{"given":"Binchi","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Virginia"}]},{"given":"Yushun","family":"Dong","sequence":"additional","affiliation":[{"name":"University of Virginia"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Virginia"}]},{"given":"Jundong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Virginia"}]}],"member":"320","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"ACM Computing Surveys","author":"Acar A.","year":"2018","unstructured":"A. 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