Federated　learning　achieves　effective　performance　in　modeling　decentralized　data.　In　practice,　client　data　are　not　well-labeled,　which　makes　it　potential　for　federated　unsupervised　learning　(FUSL)　with　non-IID　data.　However,　the　performance　of　existing　FUSL　methods　suffers　from　insufficient　representations,　i.e.,　(1)　representation　collapse　entanglement　among　local　and　global　models,　and　(2)　inconsistent　representation　spaces　among　local　models.　The　former　indicates　that　representation　collapse　in　local　model　will　subsequently　impact　the　global　model　and　other　local　models.　The　latter　means　that　clients　model　data　representation　with　inconsistent　parameters　due　to　the　deficiency　of　supervision　signals.　In　this　work,　we　propose　FedU2　which　enhances　generating　uniform　and　unified　representation　in　FUSL　with　non-IID　data.　Specifically,　FedU2　consists　of　flexible　uniform　regularizer　(FUR)　and　efficient　unified　aggregator　(EUA).　FUR　in　each　client　avoids　representation　collapse　via　dispersing　samples　uniformly,　and　EUA　in　server　promotes　unified　representation　by　constraining　consistent　client　model　updating.　To　extensively　validate　the　performance　of　FedU2,　we　conduct　both　cross-device　and　cross-silo　evaluation　experiments　on　two　benchmark　datasets,　i.e.,　CIFAR10　and　CIFAR100.　©　2024　IEEE.