A　convolutional　neural　network　training　algorithm　based　on　federated　learning　is　proposed　for　the　hybrid　beamforming　for　intelligent　reflecting　surface　assisted　communication　in　millimeter　wave　massive　multiple　input　multiple　output　system.　In　multi-user　communication　system,　the　analog　beamforming　matrix　and　intelligent　reflection　matrix　with　the　maximum　sum　rate　are　researched　by　exhaustive　search　algorithm,　which　is　set　codebooks　are　designed,　and　the　exhaustive　search　algorithm　is　used　to　search　the　analog　beamforming　matrix　and　intelligent　reflection　matrix　with　the　maximum　sum　rate　are　researched　by　exhaustive　search　algorithm,　which　is　set　as　the　training　data　label.　Then,　based　on　the　federated　learning　framework,　the　convolutional　neural　network　is　used　for　local　training　to　map　channel　matrix　to　analog　beamforming　and　intelligent　reflection　matrixes.　The　simulation　results　verity　the　feasibility　of　convolutional　neural　network　training　based　on　federated　learning.　Meanwhile,　by　comparing　the　communication　scene　with　or　without　or　randomly　intelligent　reflection　matrix,　the　proposed　algorithm　is　verified　to　be　able　to　build　an　intelligent　programmable　wireless　environment,　which　can　better　utilize　wireless　channel,　and　improve　spectral　efficiency.　©　2023　Beijing　University　of　Posts　and　Telecommunications.　All　rights　reserved.