{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T17:17:40Z","timestamp":1765041460517,"version":"3.41.2"},"reference-count":22,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB3304300","2022YFB3304303"],"award-info":[{"award-number":["2022YFB3304300","2022YFB3304303"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271128","61972073"],"award-info":[{"award-number":["62271128","61972073"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2024,5,30]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>This article addresses the issue of ensuring model accuracy and training efficiency in a constrained federated learning environment. In an actual federated learning environment, each device's software, hardware, and network conditions are heterogeneous. Some terminal devices may not be able to undertake the work assigned by the server, resulting in poor model accuracy and slower convergence speed. However, existing research cannot ensure that each terminal device participating in training can handle the workload allocated by the system without collecting too much equipment information. This article proposes the cloud\u2010edge\u2010terminal collaborative self\u2010adaptive federated learning framework (CCSFLF) to solve this problem. This framework combines the advantages of federated learning and edge computing, reduces the probability that devices cannot handle the workload of system allocation, solves the system heterogeneity, and improves the efficiency of federated learning. CCSFLF can adaptively adjust the number of training tasks for terminal devices and select valuable training participants using a terminal device selection strategy. Multiple edge servers can simultaneously aggregate local models. Cloud servers are responsible for the aggregation and task distribution of global models. The above strategy enables the framework to have a faster convergence rate and higher model accuracy. The experimental results confirm that this framework can reduce the dropout rate of terminal devices by more than 5% in heterogeneous federated learning systems, improve the model accuracy by about 2%, and reduce the training time by 1\/3 compared with similar methods, with better performance and applicability.<\/jats:p>","DOI":"10.1002\/cpe.8042","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T05:13:27Z","timestamp":1709270007000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CCSFLF: Cloud\u2010edge\u2010terminal collaborative self\u2010adaptive federated learning framework"],"prefix":"10.1002","volume":"36","author":[{"given":"Tong","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information and Software Engineering University of Electronic Science and Technology of China  Chengdu Sichuan China"}]},{"given":"Yaning","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering University of Electronic Science and Technology of China  Chengdu Sichuan China"}]},{"given":"Haonan","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, BDBC Beihang University  Beijing China"}]},{"given":"Bing","family":"Liu","sequence":"additional","affiliation":[{"name":"State Grid Chongqing Electric Power Company Nanchuan Power Supply Branch  Chongqing China"}]},{"given":"Hongyang","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Grid Chongqing Electric Power Company Nanchuan Power Supply Branch  Chongqing China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6066-981X","authenticated-orcid":false,"given":"Ruijin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering University of Electronic Science and Technology of China  Chengdu Sichuan China"}]}],"member":"311","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"e_1_2_8_2_1","first-page":"1273","volume-title":"Artificial Intelligence and Statistics","author":"McMahan B","year":"2017"},{"key":"e_1_2_8_3_1","article-title":"Privacy\u2010preserving federated learning for internet of medical things under edge computing","author":"Wang R","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"doi-asserted-by":"publisher","key":"e_1_2_8_4_1","DOI":"10.1016\/j.csa.2022.100010"},{"doi-asserted-by":"publisher","key":"e_1_2_8_5_1","DOI":"10.1109\/JIOT.2018.2874473"},{"doi-asserted-by":"publisher","key":"e_1_2_8_6_1","DOI":"10.1109\/OJCS.2020.2993259"},{"doi-asserted-by":"publisher","key":"e_1_2_8_7_1","DOI":"10.1109\/MSP.2020.2975749"},{"unstructured":"WangH YurochkinM SunY PapailiopoulosD KhazaeniY.Federated learning with matched averaging.2020. arXiv preprint arXiv:2002.06440.","key":"e_1_2_8_8_1"},{"key":"e_1_2_8_9_1","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li T","year":"2020","journal-title":"Proc Mach Learn Syst"},{"doi-asserted-by":"publisher","key":"e_1_2_8_10_1","DOI":"10.1109\/ICC.2019.8761315"},{"unstructured":"ReddiS CharlesZ ZaheerM et al.Adaptive federated optimization.2020. arXiv preprint arXiv:2003.00295.","key":"e_1_2_8_11_1"},{"doi-asserted-by":"publisher","key":"e_1_2_8_12_1","DOI":"10.1109\/IJCNN52387.2021.9533876"},{"doi-asserted-by":"publisher","key":"e_1_2_8_13_1","DOI":"10.1016\/j.neunet.2022.07.030"},{"key":"e_1_2_8_14_1","first-page":"5132","volume-title":"International Conference on Machine Learning","author":"Karimireddy SP","year":"2020"},{"unstructured":"DuanM LiuD JiX et al.FedGroup: Efficient clustered federated learning via decomposed data\u2010driven measure.2020. arXiv preprint arXiv:2010.06870.","key":"e_1_2_8_15_1"},{"key":"e_1_2_8_16_1","first-page":"1","volume-title":"ICC 2020\u20102020 IEEE International Conference on Communications (ICC)","author":"Liu L","year":"2020"},{"doi-asserted-by":"publisher","key":"e_1_2_8_17_1","DOI":"10.1109\/ICNP.2000.896303"},{"doi-asserted-by":"publisher","key":"e_1_2_8_18_1","DOI":"10.1109\/INFCOM.2000.832487"},{"doi-asserted-by":"publisher","key":"e_1_2_8_19_1","DOI":"10.1109\/35.917508"},{"unstructured":"CaldasS DudduSMK WuP et al.Leaf: A benchmark for federated settings.2018. arXiv preprint arXiv:1812.01097.","key":"e_1_2_8_20_1"},{"unstructured":"GoA BhayaniR HuangL.Twitter sentiment classification using distant supervision.2009. 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