UAV　swarms　have　crucial　applications　in　modern　military,　geological　exploration,　and　5G/6G　communication　fields.　As　communication　nodes,　drones　frequently　exchange　wireless　data　with　other　drones,　and　data　privacy　protection　is　currently　one　of　the　most　urgent　research　topics.　Based　on　this,　this　article　proposes　an　efficient　isomorphic　federated　learning　algorithm　for　unmanned　aerial　vehicle　clusters.　First,　set　the　adaptive　loss　differential　adaptive　parameter,　with　the　initial　value　set　to　a　floating　point　number　not　greater　than　0.01,　then　activate　it　with　the　Tanh　function,　participate　in　the　training　as　part　of　the　loss　function　during　the　training　process,　and　optimize　it　by　gradient　descent.　When　the　UAV　node　uploads　the　gradient　to　the　wingman,　sum　the　adaptive　loss　differential　parameter　with　the　gradient　matrix　as　a　disturbance　term;　Then,　based　on　the　dynamic　confidence　matrix,　high-quality　drones　are　selected　to　participate　in　the　gradient　security　aggregation　of　drones,　achieving　the　selection　of　high-quality　drones.　The　built-in　global　neural　network　of　the　drone　transmits　shared　parameters　indiscriminately　to　each　drone　through　broadcast　to　achieve　updates　to　the　shared　parameters.　The　comparative　experiments　of　our　algorithm　on　the　Fashion　and　Cifar10　datasets　show　that　our　algorithm　has　higher　accuracy,　with　the　highest　accuracy　improvement　of　4.42%　on　the　Mnist　dataset　and　8.22%　on　the　Cifar10　dataset.　©　2023　SPIE.