{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:28:06Z","timestamp":1775744886402,"version":"3.50.1"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20463"],"award-info":[{"award-number":["U21A20463"]}],"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":["U22B2027"],"award-info":[{"award-number":["U22B2027"]}],"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":["U23A20304"],"award-info":[{"award-number":["U23A20304"]}],"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":["2024ZYCX014"],"award-info":[{"award-number":["2024ZYCX014"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Natural Science Foundation","award":["L221014"],"award-info":[{"award-number":["L221014"]}]},{"name":"Systematic Major Project of China State Railway Group Corporation Limited","award":["P2023W002"],"award-info":[{"award-number":["P2023W002"]}]},{"name":"Hangzhou Qianjiang Distinguished Experts Programme 2024"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2025,6,1]]},"DOI":"10.1109\/jiot.2025.3533003","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T18:48:31Z","timestamp":1738090111000},"page":"16314-16324","source":"Crossref","is-referenced-by-count":10,"title":["PFedKD: Personalized Federated Learning via Knowledge Distillation Using Unlabeled Pseudo Data for Internet of Things"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3010-1392","authenticated-orcid":false,"given":"Hanxi","family":"Li","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1905-8723","authenticated-orcid":false,"given":"Guorong","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0008-596X","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"}]},{"given":"Zheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"}]},{"given":"Yongsheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Computing Technologies, China Railway Information Technology Group Corporation Ltd., Beijing, China"}]},{"given":"Fuqiang","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8278-6052","authenticated-orcid":false,"given":"Jiao","family":"Dai","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5974-1589","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.001.2100102"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3565973"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref4","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Mach. Learn. Res.","author":"McMahan"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2777990"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2020.2968505"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3324747"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/tdsc.2024.3446864"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/tdsc.2024.3445637"},{"key":"ref10","first-page":"4519","article-title":"Tighter theory for local SGD on identical and heterogeneous data","volume-title":"Proc. 23rd AISTATS","author":"Khaled"},{"key":"ref11","first-page":"1","article-title":"Federated accelerated stochastic gradient descent","volume-title":"Proc. 34th NeurIPS","author":"Yuan"},{"key":"ref12","first-page":"1","article-title":"Adaptive federated optimization","volume-title":"Proc. ICLR","author":"Reddi"},{"key":"ref13","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. ICML","author":"Karimireddy"},{"key":"ref14","first-page":"1","article-title":"Federated learning based on dynamic regularization","volume-title":"Proc. ICLR","author":"Acar"},{"key":"ref15","first-page":"1","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. 3rd MLSys","author":"Li"},{"key":"ref16","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018","journal-title":"arXiv:1806.00582"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref18","first-page":"1","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic Meta-learning approach","volume-title":"Proc. 34th NeurIPS","author":"Fallah"},{"key":"ref19","first-page":"1","article-title":"When vision transformers outperform ResNets without pretraining or strong data augmentations","volume-title":"Proc. ICLR","author":"Chen"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16867"},{"key":"ref21","first-page":"1","article-title":"Sharpness-aware minimization for efficiently improving generalization","volume-title":"Proc. ICLR","author":"Foret"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3070013"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref24","article-title":"FedMD: Heterogenous federated learning via model distillation","author":"Li","year":"2019","journal-title":"arXiv:1910.03581"},{"key":"ref25","article-title":"Communication-efficient on-device machine learning: Federated distillation and augmentation under non-IID private data","author":"Jeong","year":"2018","journal-title":"arXiv:1811.11479"},{"key":"ref26","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. ICML","author":"Zhu"},{"key":"ref27","first-page":"1","article-title":"The best of both worlds: Accurate global and Personalized models through federated learning with data-free hyper-knowledge distillation","volume-title":"Proc. ICLR","author":"Chen"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3382776"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMLCN.2023.3303292"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3347552"},{"key":"ref35","first-page":"1","article-title":"FedMix: Approximation of mixup under mean augmented federated learning","volume-title":"Proc. ICLR","author":"Yoon"},{"key":"ref36","first-page":"5905","article-title":"ASAM: Adaptive sharpness-aware minimization for scale-invariant learning of deep neural networks","volume-title":"Proc. ICML","author":"Kwon"},{"key":"ref37","first-page":"1","article-title":"An adaptive policy to employ sharpness-aware minimization","volume-title":"Proc. ICLR","author":"Jiang"},{"key":"ref38","first-page":"1","article-title":"Fantastic generalization measures and where to find them","volume-title":"Proc. ICLR","author":"Jiang"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"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","volume-title":"Handbook of Systemic Autoimmune Diseases","author":"Krizhevsky","year":"2009"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref43","article-title":"Measuring the effects of nonidentical data distribution for federated visual classification","author":"Hsu","year":"2019","journal-title":"arXiv:1909.06335"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29617"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2023.3345431"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.009.2300501"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3312059"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11008454\/10855800.pdf?arnumber=10855800","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T17:43:54Z","timestamp":1747849434000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10855800\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,1]]},"references-count":47,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2025.3533003","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,1]]}}}