With　flexible　mobility　and　broad　communication　coverage,　unmanned　aerial　vehicles　(UAVs)　have　become　an　important　extension　of　multiaccess　edge　computing　(MEC)　systems,　exhibiting　great　potential　for　improving　the　performance　of　federated　graph　learning　(FGL).　However,　due　to　the　limited　computing　and　storage　resources　of　UAVs,　they　may　not　well　handle　the　redundant　data　and　complex　models,　causing　the　inference　inefficiency　of　FGL　in　UAV-assisted　MEC　systems.　To　address　this　critical　challenge,　we　propose　a　novel　LightWeight　FGL　framework,　named　LW-FGL,　to　accelerate　the　inference　speed　of　classification　models　in　UAV-assisted　MEC　systems.　Specifically,　we　first　design　an　adaptive　information　bottleneck　(IB)　principle,　which　enables　UAVs　to　obtain　well-compressed　worthy　subgraphs　by　filtering　out　the　information　that　is　irrelevant　to　downstream　classification　tasks.　Next,　we　develop　improved　tiny　graph　neural　networks　(GNNs),　which　are　used　as　the　inference　models　on　UAVs,　thus　reducing　the　computational　complexity　and　redundancy.　Using　real-world　graph　data　sets,　extensive　experiments　are　conducted　to　validate　the　effectiveness　of　the　proposed　LW-FGL.　The　results　show　that　the　LW-FGL　achieves　higher　classification　accuracy　and　faster　inference　speed　than　state-of-the-art　methods.