Industrial　Internet　of　Things　(I-IoT)　has　become　an　emerging　driver　to　operate　industrial　systems　and　a　primary　empowerer　to　future　industries.　With　the　advanced　technologies　such　as　artificial　intelligence　(AI)　and　machine　learning　widely　used　in　IoT,　the　Industrial　IoT　is　also　witnessing　changes　driven　by　new　technologies.　Generally,　AI　technologies　require　centralized　data　collection　and　processing　to　learn　from　the　data　to　obtain　viable　models　for　application.　In　industrial　IoT,　data　security　and　privacy　problems　associated　with　reliable　and　interconnected　end　devices　are　being　faced　and　reliable　solutions　are　urgently　needed.　A　trusted　execution　environment　in　IoT　devices　is　gradually　becoming　a　feasible　approach,　and　a　distributed　solution　is　a　natural　choice　for　artificial　intelligence　technologies　in　I-IoT.　Moreover,　Federated　Learning　as　a　distributed　machine　learning　paradigm　with　privacy-preserving　properties　can　be　used　in　I-IoT.　This　paper　introduces　a　feasible　secure　data　circulation　and　sharing　scheme　for　I-IoT　devices　in　a　trusted　implementation　platform　by　employing　federated　learning.　The　suggested　framework　has　proved　to　be　efficient,　reliable,　and　accurate.