{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T09:49:36Z","timestamp":1762076976956,"version":"build-2065373602"},"reference-count":76,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,10,19]],"date-time":"2022-10-19T00:00:00Z","timestamp":1666137600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,19]]},"DOI":"10.1109\/kse56063.2022.9953790","type":"proceedings-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T21:25:53Z","timestamp":1669065953000},"page":"1-8","source":"Crossref","is-referenced-by-count":4,"title":["Federated Learning for Air Quality Index Prediction: An Overview"],"prefix":"10.1109","author":[{"given":"Duy-Dong","family":"Le","sequence":"first","affiliation":[{"name":"UEH Vinh Long Campus University of Economic Ho Chi Minh,Ho Chi Minh,Vietnam"}]},{"given":"Anh-Khoa","family":"Tran","sequence":"additional","affiliation":[{"name":"Big Data Integration Research Centers NICT,Tokyo,Japan"}]},{"given":"Minh-Son","family":"Dao","sequence":"additional","affiliation":[{"name":"Big Data Integration Research Centers NICT,Tokyo,Japan"}]},{"given":"Mohamed Saleem Haja","family":"Nazmudeen","sequence":"additional","affiliation":[{"name":"UTB School of Business Universiti Teknologi Brunei,Bandar Seri Begawan,Brunei"}]},{"given":"Viet-Tiep","family":"Mai","sequence":"additional","affiliation":[{"name":"Academy of Cryptography Techniques,Ho Chi Minh,Vietnam"}]},{"given":"Nhat-Ha","family":"Su","sequence":"additional","affiliation":[{"name":"FPT University,Ho Chi Minh,Vietnam"}]}],"member":"263","reference":[{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3181823"},{"journal-title":"ArXiv abs\/2111 08834","year":"2021","author":"nguyen","key":"ref72"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/BigData52589.2021.9671536"},{"journal-title":"Flower A Friendly Federated Learning Research Framework[J]","year":"2021","author":"beutel","key":"ref70"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-8062-5_28"},{"key":"ref74","article-title":"IoT-based Multi-modal Analysis for Smart Education: Current Status, Challenges and Opportunities","author":"gan","year":"2022","journal-title":"Proceedings of the 3rd ACM Workshop on Intelligent Cross-Data Analysis and Retrieval"},{"key":"ref39","article-title":"Edge-Assisted Hierarchical Federated Learning with Non-IID Data","author":"liu","year":"2019","journal-title":"ArXiv abs\/1905 06641"},{"key":"ref75","article-title":"COLA: Decentralized Linear Learning","author":"he","year":"2018","journal-title":"NeurIPS"},{"journal-title":"ArXiv abs\/2108 08647","year":"2020","author":"xie","key":"ref38"},{"journal-title":"Multi-modal Federated Learning on IoT Data","year":"2021","author":"zhao","key":"ref33"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/COMSNETS53615.2022.9668435"},{"journal-title":"ArXiv abs\/2004 04676","year":"2021","author":"enthoven","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3390\/s20216230"},{"key":"ref37","article-title":"Robust Multi-model Personalized Federated Learning via Model Distillation","author":"muhammad","year":"2021","journal-title":"ICA3PP"},{"key":"ref36","article-title":"Federated Multi-Task Learning","author":"smith","year":"2017","journal-title":"NIPS"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/MeMeA.2019.8802163"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.01.063"},{"key":"ref60","article-title":"Deep-dust: predicting concentrations of fine dust in Seoul using LSTM","author":"kim","year":"2019","journal-title":"Clim Inform"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2849820"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.3390\/s18072220"},{"key":"ref63","article-title":"Deep inferential spatial-temporal network for forecasting air pollution concentrations","author":"wang","year":"2018","journal-title":"Mach Learn"},{"key":"ref28","article-title":"A Review on Federated Learning and Machine Learning Approaches: Categorization","author":"ogundokun","year":"2022","journal-title":"Application Areas and Blockchain Technology Information"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1029\/2005JD006310"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref65","first-page":"3240","article-title":"Application of dynamic linear regression to improve the skill of ensemble-based deterministic ozone forecasts Atmos","volume":"40","author":"pagowski","year":"2006","journal-title":"Environ"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-12-33-2019"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1029\/2006JD007608"},{"journal-title":"TensorFlow federated Machine learning on decentralized data","year":"0","key":"ref68"},{"journal-title":"arXiv preprint arXiv 1812 01164","year":"2018","author":"caldas","key":"ref69"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2021.129072"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph15040780"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/BigData52589.2021.9671336"},{"key":"ref22","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","author":"mcmahan","year":"2017","journal-title":"AISTATS"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107261"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3075439"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3124599"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.05.093"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2019.101019"},{"key":"ref59","article-title":"A Deep Learning Approach for Forecasting Air Pollution in South Korea Using LSTM","author":"bui","year":"2018","journal-title":"Mach Learn"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.135771"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1080\/10962247.2018.1459956"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2017.08.114"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.2495\/AIR180071"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2018.10.243"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.3390\/app10061953"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/s11869-019-00696-7"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2019.09.009"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2019.06.014"},{"volume":"abs 1808 7217","journal-title":"ArXiv","year":"0","key":"ref40"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00548-1"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s12647-020-00371-8"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s40726-018-0081-0"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.4236\/ojap.2013.23007"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685991"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3074523"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3021006"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/s21134586"},{"key":"ref4","first-page":"515","article-title":"Machine learning algorithms in air quality modeling","volume":"5","author":"masih","year":"2019","journal-title":"Global Journal of Environmental Science and Management"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.glt.2020.11.001"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s40726-020-00159-z"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/atmos12060686"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.03.010"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.124023"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.5094\/APR.2015.012"},{"journal-title":"A Literature Review on Prediction of Air Quality Index and Forecasting Ambient Air Pollutants using Machine Learning Algorithms","year":"2020","author":"patil","key":"ref9"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2005.10.036"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s11135-014-0132-6"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2016.01.004"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.5094\/APR.2014.079"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.3390\/rs12244142"},{"key":"ref41","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-1191595\/v1","article-title":"A Federated Graph Neural Network Framework for Privacy-Preserving Personalization","author":"huang","year":"2022"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3390\/rs12244142"},{"key":"ref43","article-title":"STAR: Spatio-Temporal Prediction of Air Quality Using a Multi-modal Approach","author":"bui","year":"2020","journal-title":"Intell"}],"event":{"name":"2022 14th International Conference on Knowledge and Systems Engineering (KSE)","start":{"date-parts":[[2022,10,19]]},"location":"Nha Trang, Vietnam","end":{"date-parts":[[2022,10,21]]}},"container-title":["2022 14th International Conference on Knowledge and Systems Engineering (KSE)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9953734\/9953612\/09953790.pdf?arnumber=9953790","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T19:52:56Z","timestamp":1670874776000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9953790\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,19]]},"references-count":76,"URL":"https:\/\/doi.org\/10.1109\/kse56063.2022.9953790","relation":{},"subject":[],"published":{"date-parts":[[2022,10,19]]}}}