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This includes player-side and game-side characteristics, as well as country-level socio-economic statistics. Furthermore, recognizing that accurate FPS predictions require extensive user data, which raises privacy concerns, we propose a federated learning-based model to ensure user privacy. Each player and game is assigned a unique learnable knowledge kernel that gradually extracts latent features for improved accuracy. We also introduce a novel training and prediction scheme that allows these kernels to be dynamically plug-and-play, effectively addressing cold start issues. To train this model with minimal bias, we collected a large telemetry dataset from 224 countries and regions, 100,000 users, and 835 games. Our model achieved a mean Wasserstein distance of 0.469 between predicted and ground truth FPS distributions, outperforming all baseline methods.<\/jats:p>","DOI":"10.1145\/3748607","type":"journal-article","created":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T21:01:11Z","timestamp":1759698071000},"page":"329-354","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Quality of Video Gaming Experience using Global-Scale Telemetry Data and Federated Learning"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4349-252X","authenticated-orcid":false,"given":"Zhongyang","family":"Zhang","sequence":"first","affiliation":[{"name":"University of California at San Diego, San Diego, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7334-1340","authenticated-orcid":false,"given":"Jinhe","family":"Wen","sequence":"additional","affiliation":[{"name":"University of California at San Diego, La Jolla, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6296-5638","authenticated-orcid":false,"given":"Zixi","family":"Chen","sequence":"additional","affiliation":[{"name":"University of California at San Diego, La Jolla, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6465-9908","authenticated-orcid":false,"given":"Dara","family":"Arbab","sequence":"additional","affiliation":[{"name":"Arizona State University, Phoenix, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2858-9439","authenticated-orcid":false,"given":"Sruti","family":"Sahani","sequence":"additional","affiliation":[{"name":"Intel Corporation, Santa Clara, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6885-1772","authenticated-orcid":false,"given":"Kent","family":"Giard","sequence":"additional","affiliation":[{"name":"Intel Corporation, Hillsboro, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5826-1070","authenticated-orcid":false,"given":"Bijan","family":"Arbab","sequence":"additional","affiliation":[{"name":"University of California at San Diego, San Diego, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5212-2235","authenticated-orcid":false,"given":"Haojian","family":"Jin","sequence":"additional","affiliation":[{"name":"University of California at San Diego, La Jolla, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1981-6395","authenticated-orcid":false,"given":"Tauhidur","family":"Rahman","sequence":"additional","affiliation":[{"name":"University of California at San Diego, San Diego, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,10,5]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2023. 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