Data　heterogeneity　of　clients　is　one　of　the　main　factors　affecting　the　performance　of　federated　learning.　Since　the　data　distribution　on　each　client　has　large　differences,　which　makes　different　clients　participate　in　the　aggregation　of　federated　learning　with　different　effects,　the　selection　strategies　of　federated　learning　clients　are　a　hot　topic　in　recent　years.　However,　they　have　less　considered　the　impact　of　active　learning　strategies　in　federated　learning.　In　this　paper,　we　propose　a　method　to　select　clients　using　active　learning　strategies　to　address　the　impact　of　data　heterogeneity　on　federation　learning　and　improve　the　accuracy　of　the　model.　Specifically,　during　each　selection　of　clients　in　federated　learning,　a　portion　of　clients　are　selected　to　participate　in　aggregation　through　some　active　learning　strategies.We　empirically　evaluate　our　ALFL　method　on　the　publicly　available　datasets　FMNIST,　CIFAR-10　and　LEAF-synthetic　dataset.　The　experimental　results　show　that　the　method　proposed　in　this　paper　can　significantly　reduce　the　number　of　federated　learning　communication　rounds　and　improve　model　accuracy.　©　2024　IEEE.