Aiming　at　the　problem　that　the　hybrid　beamforming　in　millimeter　wave　(mm-Wave)　massive　multiple　input　multiple　output　(MIMO)　system　leads　to　excessive　communication　overhead　in　centralized　machine　learning　(CML),　a　hybrid　beamforming　and　resource　allocation　optimization　method　based　on　contract　theory　under　hierarchical　federated　learning　(FL)　was　proposed.　First,　the　system　overhead　was　calculated　for　multiple　users　in　a　hierarchical　system,　and　the　maximum　benefit　of　the　system　was　realized　by　optimizing　the　allocation　of　resources.　Then,　the　allocated　resources　were　used　to　train　the　channel　data　and　corresponding　precoder　data　in　the　back　propagation　neural　network　(BPNN).　The　model　was　aggregated　at　the　edge　server　(ES)　by　collecting　weights　and　parameters　from　users　and　uploaded　to　the　cloud　server　(CS)　to　aggregate　after　reaching　a　certain　accuracy　until　obtaining　an　optimal　model.　The　simulation　results　show　that　the　resource　optimization　greatly　reduces　the　communication　overhead,　and　hybrid　beamforming　based　on　FL　not　only　achieves　almost　the　same　sum　rate　as　to　CML,　but　also　outperforms　orthogonal　matching　pursuit　(OMP)　hybrid　beamforming　and　fully　digital　beamforming.　©　2023　Beijing　University　of　Technology.　All　rights　reserved.