The　recent　advent　of　the　Industry　4.0　era　has　led　to　the　need　to　transform　the　industrial　Internet　of　Things　towards　green,　low-carbon　and　sustainable　development.　This　is　due　to　the　fact　that　traditional　industries　consume　too　much　energy.　It　is　urgent　to　make　use　of　digital　technology　for　energy　saving　and　emission　reduction.　However,　there　are　still　some　unresolved　issues　in　the　transformation　process:　1)　the　inability　to　use　equipment　resources　thoroughly　and　efficiently,　2)　the　waste　caused　by　overly　simple　resource　management.　In　this　paper,　based　on　the　above　issues,　we　develop　the　ambient　backscatter　system　to　optimize　the　overall　resource　scheduling　scheme　and　combine　intelligent　algorithms　to　solve　the　problem　of　offloading　tasks.　The　solution　optimizes　offloading　decisions　to　minimize　system　energy　consumption　and　latency.　Meanwhile,　the　proposed　optimization　problem　is　designed　as　a　Markov　decision　process　by　combining　the　proposed　federated　learning　assigned　with　asynchronous　advantage　actor-critic　algorithm　to　obtain　the　optimal　policy.　The　final　evaluation　results　significantly　show　that　the　system　performance　indicator　based　on　our　proposed　solution　is　better　than　others.　IEEE