The　development　of　Federated　Learning　(FL)　offers　an　efficient　Machine　Learning　(ML)　approach　with　privacy　protection　to　solve　the　data　island　issue　in　distributed　Internet　of　Things　(IoT).　However,　existing　FL　frameworks　still　suffer　from　invisible　attacks　in　IoT　environments,　such　as　free-rider　attacks,　backdoor　attacks,　and　model　theft.　In　this　paper,　we　propose　a　Verifiable　Property　Federated　Learning　(VPFL)　framework　to　overcome　the　above　invisible　attacks.　We　present　a　black-box　watermarking　task　distribution　mechanism　to　prevent　free-rider　attacks　by　verifying　the　property　of　local　models.　Our　adversarial　fine-tuning　embedding　technique　can　not　only　eliminate　backdoors　in　global　models,　but　also　simultaneously　embed　white-box　watermarks　into　model　parameters　to　prevent　model　theft.　Comprehensive　experimental　evaluations　demonstrate　that　our　framework　outperforms　state-of-the-art　schemes　in　terms　of　security　and　feasibility　against　invisible　attacks.　©　2023　IEEE.