{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:48:40Z","timestamp":1782283720307,"version":"3.54.5"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Smart Grid-National Science and Technology Major Project 2030","award":["2025ZD0805000"],"award-info":[{"award-number":["2025ZD0805000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Smart Grid"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1109\/tsg.2026.3660096","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T20:44:48Z","timestamp":1770065088000},"page":"3478-3490","source":"Crossref","is-referenced-by-count":0,"title":["A Robust and Heterogeneity-Aware Federated Learning Framework With Knowledge Distillation for Cross-Regional Load Forecasting"],"prefix":"10.1109","volume":"17","author":[{"given":"Zhifeng","family":"Zuo","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6831-9948","authenticated-orcid":false,"given":"Hongxing","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6303-2307","authenticated-orcid":false,"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6115-8475","authenticated-orcid":false,"given":"Yinyin","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2023.101621"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1186\/s43067-020-00021-8"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.124179"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2022.108067"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2023.03.116"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1186\/s43067-024-00165-x"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2022.02.188"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/en15207547"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.120042"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/hsi55341.2022.9869440"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3060654"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2021.09.140"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/en18030516"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2020.3042389"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/tpwrs.2019.2943972"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2023.3266342"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2021.3098259"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2021.3066577"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2024.3514802"},{"key":"ref20","first-page":"119","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst.","author":"Blanchard"},{"key":"ref21","first-page":"1605","article-title":"Local model poisoning attacks to Byzantine-robust federated learning","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Fang"},{"key":"ref22","first-page":"10467","article-title":"Fall of empires: Breaking Byzantine-tolerant SGD by inner product manipulation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Xie"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ojcs.2020.2993259"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/worlds450073.2020.9210355"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/percomworkshops51409.2021.9431011"},{"key":"ref26","article-title":"Salvaging federated learning by local adaptation","author":"Yu","year":"2020","journal-title":"arXiv:2002.04758"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/iccsmt58129.2022.00101"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2024.107672"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/icpr48806.2021.9412599"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref31","article-title":"FedMD: Heterogenous federated learning via model distillation","author":"Li","year":"2019","journal-title":"arXiv:1910.03581"},{"key":"ref32","first-page":"8188","article-title":"Knowledge distillation in FL: A practical guide","volume-title":"Proc. 33rd Int. Joint Conf. Artif. Intell. (IJCAI)","author":"Mora"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref34","first-page":"1","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Tian"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.11.097"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/MWSCAS.2019.8885213"},{"key":"ref39","first-page":"115","article-title":"Learning precise timing with LSTM recurrent networks","volume":"3","author":"Gers","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref40","first-page":"1","article-title":"Can recurrent neural networks warp time?","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Tallec"},{"key":"ref41","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yin"},{"key":"ref42","article-title":"Byzantine-tolerant machine learning","author":"Blanchard","year":"2017","journal-title":"arXiv:1703.02757"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/icceai55464.2022.00041"},{"key":"ref44","first-page":"3395","article-title":"MPAF: Model poisoning attacks to federated learning based on fake clients","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW)","author":"Cao"}],"container-title":["IEEE Transactions on Smart Grid"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5165411\/11575304\/11370279.pdf?arnumber=11370279","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:02:52Z","timestamp":1782280972000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11370279\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":44,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tsg.2026.3660096","relation":{},"ISSN":["1949-3053","1949-3061"],"issn-type":[{"value":"1949-3053","type":"print"},{"value":"1949-3061","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]}}}