{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:02:33Z","timestamp":1779926553819,"version":"3.53.1"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013096","name":"Science and Technology Project of State Grid","doi-asserted-by":"publisher","award":["B311XT25003C"],"award-info":[{"award-number":["B311XT25003C"]}],"id":[{"id":"10.13039\/501100013096","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Standards & Interfaces"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.csi.2026.104161","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T17:07:26Z","timestamp":1773680846000},"page":"104161","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["ProtoPFL: Enhancing privacy and accuracy in federated time-series forecasting with heterogeneous data"],"prefix":"10.1016","volume":"98","author":[{"given":"Shan","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzhe","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minming","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0240-5039","authenticated-orcid":false,"given":"Wenqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.csi.2026.104161_b1","first-page":"357","article-title":"A new approach to the economic analysis of nonstationary time series and the business cycle","volume":"57","author":"Hamilton","year":"1989","journal-title":"Econ.: J. Econ. Soc."},{"issue":"6489","key":"10.1016\/j.csi.2026.104161_b2","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1126\/science.aba9757","article-title":"The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak","volume":"368","author":"Chinazzi","year":"2020","journal-title":"Science"},{"key":"10.1016\/j.csi.2026.104161_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.112896","article-title":"Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach","volume":"140","author":"Bandara","year":"2020","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"10.1016\/j.csi.2026.104161_b4","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1109\/JBHI.2017.2767063","article-title":"Deep ehr: A survey of recent advances in deep learning techniques for electronic health record (ehr) analysis","volume":"22","author":"Shickel","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.csi.2026.104161_b5","series-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2015"},{"issue":"3","key":"10.1016\/j.csi.2026.104161_b6","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"issue":"1","key":"10.1016\/j.csi.2026.104161_b7","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s40807-024-00131-1","article-title":"An adaptive prediction method for carbon emissions of power systems containing new energy based on least-squares support vector machine","volume":"11","author":"Luo","year":"2024","journal-title":"Sustain. Energy Res."},{"key":"10.1016\/j.csi.2026.104161_b8","series-title":"Long short-term memory, supervised sequence labelling with recurrent neural networks","first-page":"37","author":"Graves","year":"2012"},{"key":"10.1016\/j.csi.2026.104161_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2024.142605","article-title":"Innovative approach to daily carbon dioxide emission forecast based on ensemble of quantile regression and attention bilstm","volume":"460","author":"Zhou","year":"2024","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.csi.2026.104161_b10","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.csi.2026.104161_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110153","article-title":"Multi-scale patch transformer with adaptive decomposition for carbon emissions forecasting","volume":"146","author":"Li","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"3","key":"10.1016\/j.csi.2026.104161_b12","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.3390\/smartcities5030057","article-title":"Citizens\u2019 data privacy in china: The state of the art of the personal information protection law (pipl)","volume":"5","author":"Calzada","year":"2022","journal-title":"Smart Cities"},{"issue":"1","key":"10.1016\/j.csi.2026.104161_b13","first-page":"7","article-title":"China\u2019s pipl and dsl: Is china following the eu\u2019s approach to data protection?","volume":"5","author":"Tan","year":"2021","journal-title":"J. Data Prot. Priv."},{"key":"10.1016\/j.csi.2026.104161_b14","series-title":"Artificial Intelligence and Statistics, PMLR","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"issue":"8","key":"10.1016\/j.csi.2026.104161_b15","doi-asserted-by":"crossref","first-page":"255","DOI":"10.3390\/fi15080255","article-title":"A review of arima vs. machine learning approaches for time series forecasting in data driven networks","volume":"15","author":"Kontopoulou","year":"2023","journal-title":"Futur. Internet"},{"issue":"749","key":"10.1016\/j.csi.2026.104161_b16","first-page":"10","article-title":"A review of time series forecasting methods","volume":"11","author":"Shah","year":"2024","journal-title":"Int. J. Res. Anal. Rev."},{"key":"10.1016\/j.csi.2026.104161_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2021.117916","article-title":"A data-driven method of traffic emissions mapping with land use random forest models","volume":"305","author":"Wen","year":"2022","journal-title":"Appl. Energy"},{"issue":"3","key":"10.1016\/j.csi.2026.104161_b18","first-page":"1405","article-title":"Carbon emissions forecasting based on temporal graph transformer-based attentional neural network","volume":"24","author":"Wu","year":"2024","journal-title":"J. Comput. Methods Sci. Eng."},{"key":"10.1016\/j.csi.2026.104161_b19","series-title":"2019 IEEE International Conference on Big Data (Big Data)","first-page":"3285","article-title":"The performance of lstm and bilstm in forecasting time series","author":"Siami-Namini","year":"2019"},{"key":"10.1016\/j.csi.2026.104161_b20","series-title":"Deep transformer models for time series forecasting: the influenza prevalence case","author":"Wu","year":"2020"},{"key":"10.1016\/j.csi.2026.104161_b21","series-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"10.1016\/j.csi.2026.104161_b22","doi-asserted-by":"crossref","first-page":"2598","DOI":"10.1038\/s41598-024-82188-y","article-title":"A novel carbon emission monitoring method for power generation enterprises based on hybrid transformer model","volume":"15","author":"Jiang","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.csi.2026.104161_b23","series-title":"A dual-stage attention-based recurrent neural network for time series prediction","author":"Qin","year":"2017"},{"issue":"4","key":"10.1016\/j.csi.2026.104161_b24","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","article-title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting","volume":"37","author":"Lim","year":"2021","journal-title":"Int. J. Forecast."},{"issue":"1","key":"10.1016\/j.csi.2026.104161_b25","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","article-title":"Forecasting with artificial neural networks:: The state of the art","volume":"14","author":"Zhang","year":"1998","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.csi.2026.104161_b26","series-title":"R2n2: Residual recurrent neural networks for multivariate time series forecasting","author":"Goel","year":"2017"},{"key":"10.1016\/j.csi.2026.104161_b27","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"8432","article-title":"Fedproto: Federated prototype learning across heterogeneous clients","volume":"Vol. 36","author":"Tan","year":"2022"},{"key":"10.1016\/j.csi.2026.104161_b28","series-title":"International Conference on Machine Learning, PMLR","first-page":"2089","article-title":"Exploiting shared representations for personalized federated learning","author":"Collins","year":"2021"},{"key":"10.1016\/j.csi.2026.104161_b29","series-title":"International Conference on Machine Learning, PMLR","first-page":"9489","article-title":"Personalized federated learning using hypernetworks","author":"Shamsian","year":"2021"},{"key":"10.1016\/j.csi.2026.104161_b30","first-page":"15434","article-title":"Federated multi-task learning under a mixture of distributions","volume":"34","author":"Marfoq","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.csi.2026.104161_b31","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s11280-022-01046-x","article-title":"Multi-center federated learning: clients clustering for better personalization","volume":"26","author":"Long","year":"2023","journal-title":"World Wide Web"},{"key":"10.1016\/j.csi.2026.104161_b32","series-title":"Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security","first-page":"308","article-title":"Deep learning with differential privacy","author":"Abadi","year":"2016"},{"key":"10.1016\/j.csi.2026.104161_b33","series-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017"},{"key":"10.1016\/j.csi.2026.104161_b34","series-title":"International Conference on Learning Representations","article-title":"Learning differentially private recurrent language models","author":"McMahan","year":"2018"},{"key":"10.1016\/j.csi.2026.104161_b35","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","article-title":"Federated learning with differential privacy: Algorithms and performance analysis","volume":"15","author":"Wei","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.csi.2026.104161_b36","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"8680","article-title":"Flame: Differentially private federated learning in the shuffle model","volume":"Vol. 35","author":"Liu","year":"2021"},{"key":"10.1016\/j.csi.2026.104161_b37","series-title":"Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security (AISec)","first-page":"1","article-title":"Hybrid differential privacy in federated learning","author":"Truex","year":"2019"},{"key":"10.1016\/j.csi.2026.104161_b38","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"issue":"2","key":"10.1016\/j.csi.2026.104161_b39","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in pcm","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inform. Theory"}],"container-title":["Computer Standards & Interfaces"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0920548926000358?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0920548926000358?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T23:08:07Z","timestamp":1779923287000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0920548926000358"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":39,"alternative-id":["S0920548926000358"],"URL":"https:\/\/doi.org\/10.1016\/j.csi.2026.104161","relation":{},"ISSN":["0920-5489"],"issn-type":[{"value":"0920-5489","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ProtoPFL: Enhancing privacy and accuracy in federated time-series forecasting with heterogeneous data","name":"articletitle","label":"Article Title"},{"value":"Computer Standards & Interfaces","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.csi.2026.104161","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"104161"}}