{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T11:59:11Z","timestamp":1774699151312,"version":"3.50.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2021B0101400005"],"award-info":[{"award-number":["2021B0101400005"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402246"],"award-info":[{"award-number":["62402246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science research start-up foundation of recruiting talents of NUPT","award":["NY223188"],"award-info":[{"award-number":["NY223188"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cogn. Commun. Netw."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tccn.2025.3527711","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T15:04:16Z","timestamp":1736435056000},"page":"805-816","source":"Crossref","is-referenced-by-count":7,"title":["HiveFL: GAN-Empowered Semi-Asynchronous Federated Learning With Self-Determining Clients"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4196-3041","authenticated-orcid":false,"given":"Xiaoming","family":"He","sequence":"first","affiliation":[{"name":"School of IoT, Nanjing University of Posts and Telecommunications, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7035-6446","authenticated-orcid":false,"given":"Huawei","family":"Huang","sequence":"additional","affiliation":[{"name":"SSE, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4766-0438","authenticated-orcid":false,"given":"Baozhou","family":"Xie","sequence":"additional","affiliation":[{"name":"SSE, Sun Yat-sen University, Guangzhou, China"}]},{"given":"Chun","family":"Wang","sequence":"additional","affiliation":[{"name":"SSE, Sun Yat-sen University, Guangzhou, China"}]},{"given":"Ruixin","family":"Li","sequence":"additional","affiliation":[{"name":"SSE, Sun Yat-sen University, Guangzhou, China"}]},{"given":"Huajun","family":"Cui","sequence":"additional","affiliation":[{"name":"Digital Intelligence Research Institute, PowerChina Beijing Engineering Corporation Ltd., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-7718","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"SSE, Sun Yat-sen University, Guangzhou, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/icdcs54860.2022.00061"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref4","first-page":"1","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist. (AISTATS)","author":"McMahan"},{"key":"ref5","article-title":"Towards federated learning at scale: System design","author":"Bonawitz","year":"2019","journal-title":"arXiv:1902.01046"},{"key":"ref6","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"Konecn\u00fd","year":"2016","journal-title":"arXiv:1610.02527"},{"key":"ref7","article-title":"Federated learning for mobile keyboard prediction","author":"Hard","year":"2018","journal-title":"arXiv:1811.03604"},{"key":"ref8","first-page":"265","article-title":"Tensorflow: A system for large-scale machine learning","volume-title":"Proc. 12th USENIX Conf. Oper. Syst. Design Implem. (OSDI)","author":"Agarwal"},{"key":"ref9","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Paszke","year":"2019"},{"key":"ref10","article-title":"FedJAX: Federated learning simulation with JAX","author":"Ro","year":"2021","journal-title":"arXiv:2108.02117"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC.2016.7794955"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2944169"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20853"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3570361.3592505"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3570361.3592517"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3495243.3517017"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228925"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796982"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS54860.2022.00094"},{"key":"ref20","article-title":"Semi-synchronous federated learning","author":"Stripelis","year":"2021","journal-title":"arXiv:2102.02849"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118435"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3560905.3568538"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488815"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.2994391"},{"key":"ref25","first-page":"840","article-title":"Sageflow: Robust federated learning against both stragglers and adversaries","volume-title":"Proc. 35th Conf. Neural Inf. Process. Syst.","volume":"34","author":"Park"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/GCWkshps50303.2020.9367421"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3024629"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS47774.2020.00049"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796818"},{"key":"ref31","first-page":"1","article-title":"Advances in neural information processing systems","volume":"32","author":"Lyu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref32","first-page":"4120","article-title":"Asynchronous stochastic gradient descent with delay compensation","volume-title":"Proc. 34th Int. Conf. Mach. Learn. (ICML)","author":"Zheng"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378161"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2953131"},{"key":"ref35","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume-title":"Proc. KDD","author":"Ester"},{"key":"ref36","volume-title":"TimeGAN-PyTorch","year":"2024"},{"key":"ref37","first-page":"1","article-title":"Time-series generative adversarial networks","volume-title":"Proc. 33rd Conf. Neural Inf. Process. Syst. (NeurIPS)","volume":"32","author":"Yoon"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref40","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref41","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2012"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEEE Transactions on Cognitive Communications and Networking"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6687307\/10957747\/10835120.pdf?arnumber=10835120","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T18:41:56Z","timestamp":1762368116000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10835120\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":42,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tccn.2025.3527711","relation":{},"ISSN":["2332-7731","2372-2045"],"issn-type":[{"value":"2332-7731","type":"electronic"},{"value":"2372-2045","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}