Federated　learning　is　a　popular　framework　designed　to　perform　the　distributed　machine　learning　while　protecting　client　privacy.　However,　the　heterogeneous　data　distribution　in　real-world　environments　makes　it　difficult　to　converge　when　performing　model　training.　In　this　article,　we　propose　the　federated　gradient　scheduling　(FedGS),　an　improved　historical　gradient　sampling　utilization　method　for　optimizers　that　utilize　historical　gradients　in　the　federated　learning　to　alleviate　the　instability　problem　of　historical　gradient　information　due　to　non-IID.　FedGS　consists　of　two　main　steps　to　improve　federated　learning　performance.　First,　clustering　uses　clients＇　label　distributions,　which　relabel　clients　and　their　submitting　gradients.　Second,　sampling　gradient　clusters　to　generate　an　IID　gradient　set,　which　feeds　to　optimizers　to　derive　valid　momentum　information.　Besides,　we　introduce　differential　privacy　to　collaborate　with　FedGS　to　enhance　clients＇　privacy　protection　strength.　Compared　to　previous　non-IID　federated　learning　solutions,　our　method　can　achieve　greater　resistance　to　temporal　non-IID.　Moreover,　experiments　show　that　FedGS　can　achieve　faster　convergence　and　performance　gain　of　up　to　10%　over　existing　state-to-art　methods　in　some　scenarios.　FedGS　can　combine　with　existing　methods　easily　to　achieve　better　performance.　We　further　verify　that　our　method　has　robust　performance　gains　in　different　non-IID　scenarios,　demonstrating　the　adaptability　of　FedGS　for　different　scenarios.