{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:36:50Z","timestamp":1773232610683,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["Grant No. 61702306"],"award-info":[{"award-number":["Grant No. 61702306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013285","name":"program for professor of special appointment (eastern scholar) at shanghai institutions of higher learning","doi-asserted-by":"publisher","award":["Grant No. ts20190936"],"award-info":[{"award-number":["Grant No. ts20190936"]}],"id":[{"id":"10.13039\/501100013285","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10489-022-03578-1","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T14:02:45Z","timestamp":1654092165000},"page":"3563-3575","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["FedProLs: federated learning for IoT perception data prediction"],"prefix":"10.1007","volume":"53","author":[{"given":"Qingtian","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenzhen","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3131-2723","authenticated-orcid":false,"given":"Chao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongkui","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zedong","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,1]]},"reference":[{"issue":"10","key":"3578_CR1","doi-asserted-by":"publisher","first-page":"6532","DOI":"10.1109\/TII.2019.2945367","volume":"16","author":"M Hao","year":"2020","unstructured":"Hao M., Li H., Luo X., Xu G., Yang H., Liu S. (2020) Efficient and Privacy-Enhanced federated learning for industrial artificial intelligence. IEEE Trans Ind Inform 16(10):6532\u20136542","journal-title":"IEEE Trans Ind Inform"},{"issue":"1","key":"3578_CR2","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/JIOT.2018.2853663","volume":"6","author":"X Sun","year":"2019","unstructured":"Sun X., Gui G., Li Y., Liu R.P., An Y. (2019) Resinnet: A Novel Deep Neural Network With Feature Reuse for Internet of Things. IEEE Internet Things J 6(1):679\u2013691","journal-title":"IEEE Internet Things J"},{"issue":"5","key":"3578_CR3","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1109\/JIOT.2017.2724642","volume":"4","author":"Y Kawamoto","year":"2017","unstructured":"Kawamoto Y., Yamada N., Nishiyama H., Kato N., Shimizu Y., Zheng Y. (2017) A Feedback Control-Based Crowd Dynamics Management in IoT System. IEEE Internet Things J 4(5):1466\u20131476","journal-title":"IEEE Internet Things J"},{"issue":"3","key":"3578_CR4","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1109\/JSAC.2018.2815418","volume":"36","author":"J Ni","year":"2018","unstructured":"Ni J., Lin X., Shen X.S. (2018) Efficient and Secure Service-Oriented Authentication Supporting NetworkSlicing for 5G-Enabled IoT. IEEE J Sel Areas Commun 36(3):644\u2013657","journal-title":"IEEE J Sel Areas Commun"},{"issue":"1","key":"3578_CR5","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/COMST.2017.2762345","volume":"20","author":"J Ni","year":"2018","unstructured":"Ni J., Zhang K., Lin X., Shen X. (2018) Securing Fog Computing for Internet of Things applications: Challenges and Solutions. IEEE Commun Surv Tut 20(1):601\u2013628","journal-title":"IEEE Commun Surv Tut"},{"issue":"6","key":"3578_CR6","doi-asserted-by":"publisher","first-page":"4177","DOI":"10.1109\/TII.2019.2942190","volume":"16","author":"Y Lu","year":"2020","unstructured":"Lu Y., Huang X., Dai Y., Maharjan S., Zhang Y. (2020) Blockchain and Federated Learning for Privacy-Preserved Data Sharing in IndustrialIoT. IEEE Trans Ind Inform 16(6):4177\u20134186","journal-title":"IEEE Trans Ind Inform"},{"key":"3578_CR7","doi-asserted-by":"crossref","unstructured":"Jindal A., Aujla G. S., Kumar N., Prodan R., Obaidat M. S. (2018) DRUMS: Demand response management in a smart city using deep learning and SVR","DOI":"10.1109\/GLOCOM.2018.8647926"},{"issue":"6","key":"3578_CR8","first-page":"1027","volume":"38","author":"WS Li","year":"2018","unstructured":"Li W. S., Wang L., Chen C. (2018) Application and design of LSTM in coal mine gas prediction and warning system. Journal of Xian University of Science and Technology 38(6):1027\u20131035","journal-title":"Journal of Xian University of Science and Technology"},{"key":"3578_CR9","doi-asserted-by":"crossref","unstructured":"Wu T., Liu C., He C. (2020) Prediction of Egional Temperature Change Trend Based on LSTM Algorithm. In: Proc. IEEE (ITNEC), Chongqing, China, pp 62\u201366","DOI":"10.1109\/ITNEC48623.2020.9084842"},{"key":"3578_CR10","unstructured":"Yang Q., Liu Y., Cheng Y., Kang Y., Chen T.J., Yu H. (2020) Federated learning. Publishing House of Electronics Industry, pp 54\u201367"},{"key":"3578_CR11","unstructured":"McMahan H. B., Moore E., Ramage D., Hampson S. (2017) Communication-efficient learning of deep networks from decentralized data. In: Proc. Conf Machine Learning Research, Fort Lauderdale, FL USA"},{"issue":"2","key":"3578_CR12","doi-asserted-by":"publisher","first-page":"1C15","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q., Liu Y., Chen T. J., Tong Y. (2019) Federated machine learning: Concept and applications. ACM Trans Intell Syst technol 10(2):1C15","journal-title":"ACM Trans Intell Syst technol"},{"key":"3578_CR13","unstructured":"Cheng K., Fan T., Jin Y., Liu Y., Chen T.J., Yang Q. (2019) SecureBoost: A lossless federated learning framework, [Online]. Available: https:\/\/arxiv.org\/pdf\/1901.08755.pdf"},{"key":"3578_CR14","unstructured":"Liu Y., Chen T., Yang Q. (2018) Secure Federated Transfer Learning, [Online]. Available: https:\/\/arxiv.org\/pdf\/1812.03337.pdf"},{"issue":"5","key":"3578_CR15","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.3390\/s20051359","volume":"20","author":"H-K Lim","year":"2020","unstructured":"Lim H-K, Kim J-B, Heo J-S, Han Y-H (2020) Federated reinforcement learning for training control policies on multiple IoT devices. Sensors 20(5):1359","journal-title":"Sensors"},{"key":"3578_CR16","unstructured":"Smith V., Chiang C. K., Sanjabi M., Talwalkar A. S. (2017) Federated multi-task learning. In: Proc. Advances in Neural Information Processing Systems, Long Beach, CA USA"},{"key":"3578_CR17","doi-asserted-by":"crossref","unstructured":"Sheller M.J., Reina G.A., Edwards B., Martin J., Bakas S. (2019) Multiinstitutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. In: Proc. Int. MICCAI Brainlesion Workshop, in Lecture Notes in Computer Science, Cham, Switzerland: Springer, (11383):92C104","DOI":"10.1007\/978-3-030-11723-8_9"},{"key":"3578_CR18","unstructured":"Chen M., Mathews R., Ouyang T., Beaufays F. (2019) Federated learning of out-of-vocabulary words, [Online]. Available: https:\/\/arxiv.org\/pdf\/1903.10635.pdf"},{"key":"3578_CR19","unstructured":"Ammad-Ud-Din M., Ivannikova E., Khan S.A., Oyomno W., Fu Q., Tan K.E., Flanagan A. (2019) Federated collaborative filtering for privacy-preserving personalized recommendation system, [Online]. Available: https:\/\/arxiv.org\/pdf\/1901.09888.pdf"},{"key":"3578_CR20","doi-asserted-by":"crossref","unstructured":"Ding Z., Gao X., Xu J., Wu H. (2013) IOT-StatisticDB: A General Statistical Database Cluster Mechanism for Big Data Analysis in the Internet of Things, in proc. IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, Beijing, 535\u2013543","DOI":"10.1109\/GreenCom-iThings-CPSCom.2013.104"},{"key":"3578_CR21","doi-asserted-by":"publisher","first-page":"101181","DOI":"10.1016\/j.phycom.2020.101181","volume":"43","author":"K He","year":"2020","unstructured":"He K, Wang Z, Li D, Zhu F, Fan L (2020) Ultra-reliable MU-MIMO detector based on deep learning for 5G\/B5G-enabled IoT. Physical Communication 43:101181. ISSN 1874-4907, https:\/\/doi.org\/10.1016\/j.phycom.2020.101181","journal-title":"Physical Communication"},{"issue":"3","key":"3578_CR22","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1109\/TBC.2020.2985592","volume":"66","author":"J Xia","year":"2020","unstructured":"Xia J., Deng D., Fan D. (2020) A Note on Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters. IEEE Transactions on Broadcasting 66(3):744\u2013745. https:\/\/doi.org\/10.1109\/TBC.2020.2985592https:\/\/doi.org\/10.1109\/TBC.2020.2985592","journal-title":"IEEE Transactions on Broadcasting"},{"key":"3578_CR23","doi-asserted-by":"crossref","unstructured":"Lee M., Hwang J., Yoe H. (2013) Agricultural Production System Based on IoT. In: Proc International Conference on Computational Science and Engineering, Sydney, NSW, IEEE 16th, pp 833\u2013837","DOI":"10.1109\/CSE.2013.126"},{"issue":"3","key":"3578_CR24","doi-asserted-by":"publisher","first-page":"4242","DOI":"10.1109\/JIOT.2018.2875715","volume":"6","author":"Y Chen","year":"2019","unstructured":"Chen Y., Zhang N., Zhang Y., Chen X. (2019) Dynamic Computation Offloading in Edge Computing for Internet of Things. IEEE Internet Things J 6(3):4242\u20134251","journal-title":"IEEE Internet Things J"},{"key":"3578_CR25","doi-asserted-by":"crossref","unstructured":"Mao Y., Yang F., Wang C. (2011) Application of BP network to short-term power load forecasting considering weather factor, in Proc International Conference on Electric Information and Control Engineering, Wuhan, pp 172\u2013175","DOI":"10.1109\/ICEICE.2011.5777343"},{"issue":"9","key":"3578_CR26","first-page":"33","volume":"23","author":"R Guo","year":"2013","unstructured":"Guo R., Xu G.L. (2013) Research on multi-sensor prediction model of coal mine gas concentration based on information fusion and GA-SVM. Chinese Journal of Safety Science 23(9):33\u201338","journal-title":"Chinese Journal of Safety Science"},{"key":"3578_CR27","doi-asserted-by":"crossref","unstructured":"Akpinar M., Yumusak N. (2013) Forecasting household natural gas consumption with ARIMA model: A case study of removing cycle. In: Proc International Conference on Application of Information and Communication Technologies, Baku, pp 1\u20136","DOI":"10.1109\/ICAICT.2013.6722753"},{"key":"3578_CR28","doi-asserted-by":"crossref","unstructured":"Farrugia R.A. (2012) Improving motion vector prediction using linear regression, 5th International Symposium on Communications, Control and Signal Processing, Rome, pp 1\u20134","DOI":"10.1109\/ISCCSP.2012.6217750"},{"key":"3578_CR29","unstructured":"Yang H. (2017) Research on weather forecasting based on deep learning, M.S. thesis, Dept, Harbin Institute of Technology, China"},{"issue":"1","key":"3578_CR30","first-page":"54","volume":"45","author":"YX Zhao","year":"2020","unstructured":"Zhao Y. X., Yang Z. L., Ma B. J., Song H. H., Yang D. H. (2020) Deep learning prediction and model generalization of ground pressure for deep longwall face with large mining height. Journal of China Coal Society 45(1):54\u201365","journal-title":"Journal of China Coal Society"},{"issue":"2","key":"3578_CR31","first-page":"213","volume":"35","author":"YJ Yang","year":"2010","unstructured":"Yang Y. J., Wang D. C., Chen S. J., et al. (2010) AE Predicting study on compression and fracture of limestone sample based on discrete wavelet analysis. Journal of China Coal Society 35(2):213\u2013 217","journal-title":"Journal of China Coal Society"},{"key":"3578_CR32","unstructured":"Taylor S.J., Letham B. (2019) Forecasting at scale, [Online]. Available: https:\/\/facebookincubator.github.io\/prophet\/static\/prophet_paper_20170113.pdf"},{"key":"3578_CR33","doi-asserted-by":"crossref","unstructured":"Gong F., Han N., Li D., Tian S. (2020) Trend Analysis of Building Power Consumption Based on Prophet Algorithm, 2020 Asia Energy and Electrical Engineering Symposium (AEEES), Chengdu, China, pp 1002\u20131006","DOI":"10.1109\/AEEES48850.2020.9121548"},{"issue":"1","key":"3578_CR34","first-page":"75","volume":"50","author":"LP Li","year":"2019","unstructured":"Li L. P., Duan G. H., Wang J. X. (2019) Reserve prediction of bank outlets based on prophet framework. Journal of Central South University (Science and Technology) 50(1):75\u201382","journal-title":"Journal of Central South University (Science and Technology)"},{"key":"3578_CR35","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"B Hochreiter","year":"1997","unstructured":"Hochreiter B., Schmidhuber J. (1997) Long short term memory. Neural Comput 9:1735\u20131780","journal-title":"Neural Comput"},{"key":"3578_CR36","unstructured":"Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. [Online]. Available: https:\/\/arxiv.org\/pdf\/1506.00019.pdf"},{"key":"3578_CR37","doi-asserted-by":"publisher","first-page":"17351C17360","DOI":"10.1007\/s00521-020-04867-x","volume":"32","author":"IE Livieris","year":"2020","unstructured":"Livieris IE, Pintelas E, Pintelas P (2020) A CNNCLSTM model for gold price time-series forecasting. Neural Comput and Applic 32:17351C17360","journal-title":"Neural Comput and Applic"},{"key":"3578_CR38","doi-asserted-by":"crossref","unstructured":"Box GEP, Jenkins GM (2010) Time series analysis : forecasting and control. Journal of Time, 31(3)","DOI":"10.1111\/j.1467-9892.2009.00643.x"},{"key":"3578_CR39","doi-asserted-by":"crossref","unstructured":"Shuwen J, Tingting Y (2021) Research on Stock Price Forecasting Based on BP Neural Network. Advances in Artificial Intelligence and Security, pp 663\u2013673","DOI":"10.1007\/978-3-030-78615-1_58"},{"key":"3578_CR40","unstructured":"Yu A, Lai WL, Payor J (2015) Efficient Integer Vector Homomorphic Encryption. http:\/\/courses.csail.mit.edu\/6.857\/2015\/files\/yu-lai-payor.pdf"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03578-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-022-03578-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-022-03578-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T14:27:44Z","timestamp":1700663264000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-022-03578-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,1]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["3578"],"URL":"https:\/\/doi.org\/10.1007\/s10489-022-03578-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,1]]},"assertion":[{"value":"1 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}