{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T21:03:31Z","timestamp":1758056611330,"version":"3.44.0"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62172088","62272290"],"award-info":[{"award-number":["62172088","62272290"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Shanghai Municipality","award":["21ZR1400400"],"award-info":[{"award-number":["21ZR1400400"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Auton. Adapt. Syst."],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>\n            Connected vehicles due to the high mobility and dynamic network topologies of connected vehicles require accurate QoS that includes high throughput and low latency to assess satisfactory QoE. Existing methods mainly focus on centralized QoS prediction while paying little attention to distributed mobile QoS prediction, making it challenging to protect user privacy information when invoking Web services. Moreover, even though some advanced centralized methods can be transformed into federated architectures, they often face difficulty in capturing latent feature representations of users and services and learning personalized prediction layers between them due to the heterogeneity of the QoS dataset. To address the above issues, we propose a novel framework for distributed QoS prediction, called Combining Personalized\n            <jats:bold>Federated Hypernetworks and Shared Residual Learning for Distributed QoS Prediction (FHR-DQP)<\/jats:bold>\n            . FHR-DQP adopts the\n            <jats:bold>federated averaging (FedAvg)<\/jats:bold>\n            to aggregate location-aware residual shared feature information across all clients. Additionally, a hypernetwork is leveraged to generate personalized networks for user-service QoS prediction in each client. These components are integrated as a hybrid framework that performs training using a federated approach and makes personalized QoS predictions within each client. Extensive experiments are conducted on a real-world benchmark QoS dataset called WS-DREAM, containing nearly 2,000,000 historical QoS invocation records. Compared with both centralized and federated competing baselines, the results demonstrate that FHR-DQP achieves the highest performance for distributed QoS prediction, when it provides privacy-preserving of users\u2019 QoS invocations.\n          <\/jats:p>","DOI":"10.1145\/3709141","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T10:30:06Z","timestamp":1734690606000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Combining Personalized Federated Hypernetworks and Shared Residual Learning for Distributed QoS Prediction"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7865-8158","authenticated-orcid":false,"given":"Guobing","family":"Zou","sequence":"first","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8355-0259","authenticated-orcid":false,"given":"Shiyi","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0833-958X","authenticated-orcid":false,"given":"Shaogang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1510-2478","authenticated-orcid":false,"given":"Shengxiang","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3896-7049","authenticated-orcid":false,"given":"Song","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering and Science, Shanghai University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5931-9006","authenticated-orcid":false,"given":"Yanglan","family":"Gan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Donghua University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2096-4978","authenticated-orcid":false,"given":"Bofeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3704-4432","authenticated-orcid":false,"given":"Yixin","family":"Chen","sequence":"additional","affiliation":[{"name":"Washington University, St. Louis, MO, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_2_2","DOI":"10.1007\/978-3-031-48424-7_13"},{"doi-asserted-by":"publisher","key":"e_1_3_2_3_2","DOI":"10.1016\/j.asoc.2024.111577"},{"unstructured":"Manoj Ghuhan Arivazhagan Vinay Aggarwal Aaditya Kumar Singh and Sunav Choudhary. 2019. 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