This　paper　presents　the　concept　and　studies　of　federated　deep　reinforcement　learning　(DRL)　based　anti-jamming　communication　in　softwarization　UAV　network　(SUNET).　First,　task-driven　SUNET　framework　is　presented　and　joint　beamforming　and　power　control　(JBPC)　based　multidomain　anti-jamming　model　is　built　to　balance　the　spectrum　efficiency　(SE)　and　energy　efficiency　(EE)　of　the　SUENT.　Then　a　weighted　dueling　DQN　(wDDQN)　learning　algorithm　with　upper　confidence　bound　(UCB)　action　exploration　is　provided　to　handle　the　formulated　model.　Further,　we　propose　federated　wDDQN-UCB　(F-wDDQN)　based　JBPC　anti-jamming　strategy　to　tackle　the　challenge　of　agent　training　would　consume　a　large　of　communication　resources,　and　design　the　link　state　aware　quantity　of　service　(LSAQ)　routing　of　SUNET　to　reduce　transmission　delay　of　the　model　parameters　during　F-wDDQN　training.　Simulation　results　validate　that　the　spectrum-energy　efficiency　and　convergence　performance　achieved　by　the　F-wDDQN　based　JBPC　anti-jamming　strategy　is　superior　to　existing　DRL　methods,　and　the　LSAQ　routing　is　benefit　to　reduce　the　transmission　delay　of　the　F-wDDQN　learning　strategy　and　accelerate　its　convergence.