Federated　Learning　(FL)　continues　to　make　significant　advances,　solving　model　sharing　under　privacy-preserving.　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.　As　such,　a　natural　problem　is　how　to　leverage　unlabeled　data　among　multiple　clients　to　optimize　sharing　model.　To　address　this　shortcoming,　we　propose　Federated　Contrastive　Learning　(FedCL),　an　efficient　federated　learning　method　for　unsupervised　image　classification.　The　proposed　FedCL　can　be　summarized　in　three　steps:　distributed　federated　pretraining　of　the　local　model　using　contrastive　learning,　supervised　fine-tuning　on　a　server　with　few　labeled　data,　and　distillation　with　unlabeled　examples　on　each　client　for　refining　and　transferring　the　personalized-specific　knowledge.　Extensive　experiments　show　that　our　method　outperforms　all　baseline　methods　by　large　margins,　including　69.32%　top-1　accuracy　on　CIFAR-10,　85.75%　on　SVHN,　and　74.64%　on　Mini-ImageNet　with　the　only　use　of　1%　labels.