{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T13:26:16Z","timestamp":1769347576358,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T00:00:00Z","timestamp":1714780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources","award":["MESTA-2023-B00"],"award-info":[{"award-number":["MESTA-2023-B00"]}]},{"name":"Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources","award":["62192712"],"award-info":[{"award-number":["62192712"]}]},{"name":"Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources","award":["JCKYS2022604SSJS007"],"award-info":[{"award-number":["JCKYS2022604SSJS007"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["MESTA-2023-B00"],"award-info":[{"award-number":["MESTA-2023-B00"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62192712"],"award-info":[{"award-number":["62192712"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["JCKYS2022604SSJS007"],"award-info":[{"award-number":["JCKYS2022604SSJS007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Stable Supporting Fund of National Key Laboratory of Underwater Acoustic Technology","award":["MESTA-2023-B00"],"award-info":[{"award-number":["MESTA-2023-B00"]}]},{"name":"Stable Supporting Fund of National Key Laboratory of Underwater Acoustic Technology","award":["62192712"],"award-info":[{"award-number":["62192712"]}]},{"name":"Stable Supporting Fund of National Key Laboratory of Underwater Acoustic Technology","award":["JCKYS2022604SSJS007"],"award-info":[{"award-number":["JCKYS2022604SSJS007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A space\u2013air\u2013ground\u2013sea integrated network (SAGSIN) is a promising heterogeneous network framework for the next generation mobile communications. Moreover, federated learning (FL), as a widely used distributed intelligence approach, can improve advanced network performance. In view of the combination and cooperation of SAGSINs and FL, an FL-based SAGSIN framework faces a number of unprecedented challenges, not only from the communication aspect but also on the security and privacy side. Motivated by these observations, in this article, we first give a detailed state-of-the-art review of recent progress and ongoing research works on FL-based SAGSINs. Then, the challenges of FL-based SAGSINs are discussed. After that, for different service demands, basic applications are introduced with their benefits and functions. In addition, two case studies are proposed, in order to improve SAGSINs\u2019 communication efficiency under a significant communication latency difference and to protect user-level privacy for SAGSIN participants, respectively. Simulation results show the effectiveness of the proposed algorithms. Moreover, future trends of FL-based SAGSINs are discussed.<\/jats:p>","DOI":"10.3390\/rs16091640","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T13:27:00Z","timestamp":1715002020000},"page":"1640","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Space\u2013Air\u2013Ground\u2013Sea Integrated Network with Federated Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5079-3887","authenticated-orcid":false,"given":"Hao","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou 510725, China"},{"name":"The Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China"}]},{"given":"Fei","family":"Ji","sequence":"additional","affiliation":[{"name":"The Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China"},{"name":"School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China"}]},{"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6355-9061","authenticated-orcid":false,"given":"Kexing","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1031-492X","authenticated-orcid":false,"given":"Fangjiong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TCCN.2017.2758370","article-title":"An Introduction to Deep Learning for the Physical Layer","volume":"3","author":"Hoydis","year":"2017","journal-title":"IEEE Trans. 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