{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T14:01:15Z","timestamp":1784556075940,"version":"3.55.0"},"reference-count":52,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"24","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021ZD0110900"],"award-info":[{"award-number":["2021ZD0110900"]}],"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":["62106061"],"award-info":[{"award-number":["62106061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972114"],"award-info":[{"award-number":["61972114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["FRFCU5710010521"],"award-info":[{"award-number":["FRFCU5710010521"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Programs for Science and Technology Development of Heilongjiang Province","award":["2021ZXJ05A03"],"award-info":[{"award-number":["2021ZXJ05A03"]}]},{"DOI":"10.13039\/100017366","name":"Key Research and Development Program of Heilongjiang Province","doi-asserted-by":"publisher","award":["2022ZX01A22"],"award-info":[{"award-number":["2022ZX01A22"]}],"id":[{"id":"10.13039\/100017366","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005046","name":"National Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["YQ2019F007"],"award-info":[{"award-number":["YQ2019F007"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2023,12,15]]},"DOI":"10.1109\/jiot.2023.3303889","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T17:45:36Z","timestamp":1691689536000},"page":"22530-22541","source":"Crossref","is-referenced-by-count":29,"title":["Data-Augmentation-Based Federated Learning"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-2115","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3767-2838","authenticated-orcid":false,"given":"Qingying","family":"Hou","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3437-8899","authenticated-orcid":false,"given":"Tingting","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1263-9907","authenticated-orcid":false,"given":"Siyao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Harbin Institute of Technology, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000045"},{"key":"ref2","first-page":"10","article-title":"The EU general data protection regulation (GDPR)","volume-title":"A Practical Guide","volume":"10","author":"Voigt","year":"2017"},{"key":"ref3","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Stat.","author":"McMahan"},{"key":"ref4","article-title":"Applied federated learning: Improving Google keyboard query suggestions","author":"Yang","year":"2018","journal-title":"arXiv:1812.02903"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref8","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref9","article-title":"Federated learning with additional mechanisms on clients to reduce communication costs","author":"Yao","year":"2019","journal-title":"arXiv:1908.05891"},{"key":"ref10","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref12","first-page":"28663","article-title":"Breaking the centralized barrier for cross-device federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Karimireddy"},{"key":"ref13","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018","journal-title":"arXiv:1806.00582"},{"key":"ref14","first-page":"1","article-title":"FedBE: Making Bayesian model ensemble applicable to federated learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Chen"},{"key":"ref15","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Lin"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/205"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3218315"},{"key":"ref18","first-page":"1","article-title":"Mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref19","first-page":"1","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Berthelot"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013714"},{"key":"ref21","first-page":"1","article-title":"FedMix: Approximation of mixup under mean augmented federated learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Yoon"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_5"},{"key":"ref23","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019","journal-title":"arXiv:1909.06335"},{"key":"ref24","first-page":"1","article-title":"Federated learning based on dynamic regularization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Acar"},{"key":"ref25","article-title":"FedCD: Improving performance in non-iid federated learning","author":"Kopparapu","year":"2020","journal-title":"arXiv:2006.09637"},{"key":"ref26","first-page":"1","article-title":"Federated learning via posterior averaging: A new perspective and practical algorithms","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Al-Shedivat"},{"key":"ref27","first-page":"1","article-title":"FedBN: Federated learning on non-IID features via local batch normalization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref28","first-page":"4387","article-title":"The non-iid data quagmire of decentralized machine learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hsieh"},{"key":"ref29","article-title":"Faster on-device training using new federated momentum algorithm","author":"Huo","year":"2020","journal-title":"arXiv:2002.02090"},{"key":"ref30","first-page":"1","article-title":"Adaptive federated optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Reddi"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/324"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9149323"},{"key":"ref33","article-title":"Federated learning with GAN-based data synthesis for non-iid clients","volume-title":"arXiv:2206.05507","author":"Li","year":"2022"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-021-10144-6"},{"key":"ref35","article-title":"Distilled one-shot federated learning","author":"Zhou","year":"2020","journal-title":"arXiv:2009.07999"},{"key":"ref36","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2021.3129371"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref39","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Verma"},{"key":"ref40","first-page":"5275","article-title":"Puzzle mix: Exploiting saliency and local statistics for optimal mixup","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim"},{"key":"ref41","article-title":"FMix: Enhancing mixed sample data augmentation","author":"Harris","year":"2020","journal-title":"arXiv:2002.12047"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/Blockchain50366.2020.00019"},{"key":"ref43","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3545008.3545013"},{"key":"ref45","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref46","article-title":"CINIC-10 is not ImageNet or CIFAR-10","author":"Darlow","year":"2018","journal-title":"arXiv:1810.03505"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.2307\/2684710"},{"key":"ref48","article-title":"C-Mixup: Improving generalization in regression","author":"Yao","year":"2022","journal-title":"arXiv:2210.05775"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.463"},{"key":"ref50","first-page":"1","article-title":"FedML: A research library and benchmark for federated machine learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"He"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"ref52","first-page":"1","article-title":"Generalized cross entropy loss for training deep neural networks with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Zhang"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488907\/10353047\/10214273.pdf?arnumber=10214273","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T01:12:22Z","timestamp":1705021942000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10214273\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,15]]},"references-count":52,"journal-issue":{"issue":"24"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2023.3303889","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,15]]}}}