Federated　learning　(FL)　provides　a　novel　framework　to　collaboratively　train　a　shared　model　in　a　distribution　fashion　by　virtue　of　a　central　server.　However,　FL　is　inappropriate　for　a　serverless　scenario　and　also　suffers　from　some　major　drawbacks　in　Industrial　Internet　of　Things　(IIoT)　networks,　such　as　unresilience　to　network　failures　and　communication　bottleneck　effect.　In　this　article,　we　propose　a　novel　decentralized　federated　learning　(DFL)　approach　for　IIoT　devices　to　achieve　model　consensus　by　exchanging　model　parameters　only　with　their　neighbors　rather　than　a　central　server.　We　firstly　formulate　the　problem　of　model　consensus　in　DFL　as　a　fastest　mixing　Markov　chain　problem　and　then　optimize　the　consensus　matrix　to　improve　the　convergence　rate.　Meanwhile,　a　practical　medium　access　control　protocol　with　time　slotted　channel　hopping　is　taken　into　account　to　implement　the　proposed　approach.　Furthermore,　we　also　propose　an　accumulated　update　compression　method　to　alleviate　communication　cost.　Finally,　extensive　simulation　results　demonstrate　that　the　proposed　approach　improves　accuracy　and　reduces　communication　cost　especially　under　the　nonindependent　identically　distribution　data　distribution.