Remote　sensing　images　are　usually　distributed　in　different　departments　and　contain　private　information,　so　they　normally　cannot　be　available　publicly.　However,　it　is　a　trend　to　jointly　use　remote　sensing　images　from　different　departments,　because　it　normally　enables　the　model　to　capture　more　information　and　remote　sensing　image　analysis　based　on　deep　learning　generally　requires　lots　of　training　data.　To　address　the　above　problem,　in　this　paper,　we　apply　a　distributed　asynchronized　discriminator　GAN　framework　(DGAN)　to　jointly　learn　remote　sensing　images　from　different　client　nodes.　The　DGAN　is　composed　of　multiple　distributed　discriminators　and　a　central　generator,　and　only　the　synthetic　remote　sensing　images　generated　by　the　DGAN　are　used　to　train　a　semantic　segmentation　model.　Based　on　DGAN,　we　establish　an　experimental　platform　composed　of　multiple　different　hosts,　which　adopts　socket　and　multi-process　technology　to　realize　asynchronous　communication　between　hosts,　and　visualize　the　training　and　testing　process.　During　DGAN　training,　instead　of　original　remote　sensing　images　or　convolutional　network　model　information,　only　synthetic　images,　losses　and　labeled　images　are　exchanged　between　nodes.　Therefore,　the　DGAN　well　protects　the　privacy　and　security　of　the　original　remote　sensing　images.　We　verify　the　performance　of　the　DGAN　on　three　remote　sensing　image　datasets　(City-OSM,　WHU　and　Kaggle　Ship).　In　the　experiments,　we　take　different　distributions　of　remote　sensing　images　in　client　nodes　into　consideration.　The　experiments　show　that　the　DGAN　has　a　great　capacity　for　distributed　remote　sensing　image　learning　without　sharing　the　original　remote　sensing　images　or　the　convolutional　network　model.　Moreover,　compared　with　a　centralized　GAN　trained　on　all　remote　sensing　images　collected　from　all　client　nodes,　the　DGAN　can　achieve　almost　the　same　performance　in　semantic　segmentation　tasks　for　remote　sensing　images.　　©　2022　ACM.