Due　to　resource　constraints　and　working　surroundings,　many　IIoT　nodes　are　easily　hacked　and　turn　into　zombies　from　which　to　launch　attacks.　It　is　challenging　to　detect　such　networked　zombies　rooted　behind　the　Internet　for　any　individual　defender.　In　this　article,　we　combine　federated　learning　(FL)　and　fog/edge　computing　to　combat　malicious　codes.　Our　protocol　trains　a　global　optimized　model　based　on　distributed　datasets　of　collaborators　while　removing　the　data　and　communication　constraints.　The　FL-based　detection　protocol　maximizes　the　values　of　distributed　data　samples,　resulting　in　an　accurate　model　timely.　On　top　of　the　protocol,　we　place　mitigation　intelligence　in　a　distributed　and　collaborative　manner.　Our　approach　improves　accuracy,　eliminates　mitigation　time,　and　enlarges　attackers＇　expense　within　a　defense　alliance.　Comprehensive　evaluations　confirm　that　the　cost　incurred　is　2.7　times　larger,　the　mitigation　response　time　is　72%　lower,　and　the　accuracy　is　47%　higher　on　average.　Besides,　the　protocol　evaluation　shows　the　detection　accuracy　is　approximately　98%　in　the　FL,　which　is　almost　the　same　as　centralized　training.