Federated　learning　(FL)　lately　has　shown　much　promise　in　improving　the　shared　model　and　preserving　data　privacy.　However,　these　existing　methods　are　only　of　limited　utility　in　the　Internet　of　Things　(IoT)　scenarios,　as　they　either　heavily　depend　on　high-quality　labeled　data　or　only　perform　well　under　idealized　conditions,　which　typically　cannot　be　found　in　practical　applications.　In　this　article,　we　propose　a　novel　federated　unsupervised　learning　method　for　image　classification　without　the　use　of　any　ground　truth　annotations.　In　IoT　scenarios,　a　big　challenge　is　that　decentralized　data　among　multiple　clients　is　normally　nonindependent　and　identically　distributed　(non-IID),　leading　to　performance　degradation.　To　address　this　issue,　we　further　propose　a　dynamic　update　mechanism　that　can　decide　how　to　update　the　local　model　based　on　weights　divergence.　Extensive　experiments　show　that　our　method　outperforms　all　baseline　methods　by　large　margins,　including　+6.67%　on　CIFAR-10,　+5.15%　on　STL-10,　and　+8.44%　on　SVHN　in　terms　of　classification　accuracy.　In　particular,　we　obtain　promising　results　on　Mini-ImageNet　and　COVID-19　data　sets　and　outperform　several　federated　unsupervised　learning　methods　under　non-IID　settings.