{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T10:43:01Z","timestamp":1769251381219,"version":"3.49.0"},"reference-count":15,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internet Technology Letters"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Transformer\u2010based model has achieved significant success for consumer price index (CPI) prediction task. Its higher model complexity and limited local modeling ability require more training samples. However, privacy protection in different economic sectors severely restricts predictive performance. Although distributed learning can achieve cross\u2010institutional collaborative training, resource\u2010constrained clients pose significant challenges for the deployment of deep models. To this end, this paper proposes a 1D convolution and Swin Transformer hybrid network based on federated learning for CPI prediction, in which the 1D convolution operations focus on local features and the Swin Transformer achieves long\u2010distance dependency modeling with lower computational complexity. In addition, we introduce federated learning mechanisms to further improve model training in privacy protection scenarios. The experimental results on the public dataset indicate that the proposed model achieves the highest predictive performance by introducing the distributed learning measures.<\/jats:p>","DOI":"10.1002\/itl2.70207","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T17:50:38Z","timestamp":1765561838000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Consumer Price Index Forecasting Based on Federated Learning and a Hybrid Deep Neural Network for Economic Activity Evaluation"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1454-2769","authenticated-orcid":false,"given":"Qingzhen","family":"Meng","sequence":"first","affiliation":[{"name":"Jilin Agricultural Science and Technology College  Jilin China"}]},{"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"Universiti Malaya  Kuala Lumpur Malaysia"}]}],"member":"311","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICAID65275.2025.11034515"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.9734\/arjom\/2025\/v21i1880"},{"key":"e_1_2_9_4_1","first-page":"485","volume-title":"Application of Big Data Analysis in Macroeconomic Models: A Case Study on Consumer Price Index (CPI) Forecasting","author":"Jiang Z.","year":"2025"},{"key":"e_1_2_9_5_1","first-page":"12","article-title":"Cuban Consumer Price Index Forecasting Trough Transformer With Attention","volume":"12","author":"Rosado R.","year":"2023","journal-title":"Journal of Automation, Mobile Robotics and Intelligent Systems"},{"issue":"55","key":"e_1_2_9_6_1","first-page":"1","article-title":"From Market Volatility to Predictive Insight: An Adaptive Transformer\u2013RL Framework for Sentiment\u2010Driven Financial Time\u2010Series Forecasting","volume":"7","author":"Song Z.","year":"2025","journal-title":"Forecast"},{"key":"e_1_2_9_7_1","first-page":"1","volume-title":"Consumer Price Index Prediction Using Long Short Term Memory (LSTM) Based Cloud Computing","author":"Zahara S.","year":"2020"},{"key":"e_1_2_9_8_1","first-page":"1","volume-title":"Selection of a Forecasting Model for Russia Consumer Price Index Based on the Application of Analytic Hierarchy Process","author":"Kasyan E.","year":"2022"},{"issue":"8","key":"e_1_2_9_9_1","first-page":"1","article-title":"Prediction of CPI in Saudi Arabia: Holt's Linear Trend Approach","volume":"9","author":"Ali A.","year":"2020","journal-title":"Transportation"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11135-022-01328-6"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.econlet.2004.09.003"},{"key":"e_1_2_9_12_1","first-page":"1","volume-title":"Modeling Consumer Price Index: A Machine Learning Approach","author":"Sarangi P. K.","year":"2022"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3718491.3718665"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3358452"},{"key":"e_1_2_9_15_1","first-page":"10713","article-title":"Model\u2010Contrastive Federated Learning","author":"Li Q.","year":"2021","journal-title":"arXiv:2103.16257"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-89691-1_4"}],"container-title":["Internet Technology Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70207","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/itl2.70207","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/itl2.70207","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T03:41:03Z","timestamp":1769139663000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/itl2.70207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,12]]},"references-count":15,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1002\/itl2.70207"],"URL":"https:\/\/doi.org\/10.1002\/itl2.70207","archive":["Portico"],"relation":{},"ISSN":["2476-1508","2476-1508"],"issn-type":[{"value":"2476-1508","type":"print"},{"value":"2476-1508","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,12]]},"assertion":[{"value":"2025-07-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70207"}}