UAV-assisted　mobile　edge　computing　(UAV-MEC)　has　been　proposed　to　offer　computing　resources　for　smart　devices　and　user　equipment.　UAV　cluster　aided　MEC　rather　than　one　UAV-aided　MEC　as　edge　pool　is　the　newest　edge　computing　architecture.　Unfortunately,　the　data　packet　exchange　during　edge　computing　within　the　UAV　cluster　hasn't　received　enough　attention.　UAVs　need　to　collaborate　for　the　wide　implementation　of　MEC,　relying　on　the　gossip-based　broadcast　protocol.　However,　gossip　has　the　problem　of　long　propagation　delay,　where　the　forwarding　probability　and　neighbors　are　two　factors　that　are　difficult　to　balance.　The　existing　works　improve　gossip　from　only　one　factor,　which　cannot　select　suitable　forwarding　probability　and　avoid　redundant　messages.　Besides,　these　schemes　do　not　consider　the　historical　packet　reception　of　new　neighbors　when　UAVs　fly　around,　which　decreases　forwarding　efficiency.　To　solve　these　problems,　we　first　propose　a　data　structure　called　Bitgraph　that　can　record　the　historical　packet　reception　of　UAVs.　Then,　we　formulate　gossip　broadcasting　as　a　partially　observable　Markov　decision　process.　Based　on　Bitgraph,　we　design　the　reward　function.　Finally,　we　design　a　multi-agent　reinforcement　learning　algorithm,　Branching　Deep　Graph　Network　(BDGN),　which　simultaneously　makes　decisions　on　forwarding　probability　and　neighbors.　Extensive　experiments　illustrate　that　our　proposal　gets　more　than　29%　advantage　in　terms　of　the　propagation　delay　and　20%　advantage　in　terms　of　the　redundant　messages　compared　to　the　existing　works.　IEEE