Federated　learning　enables　participating　devices　to　learn　a　shared　model.　However,　in　real-world　application　scenarios,　participating　devices　exhibit　significant　heterogeneity　in　terms　of　data　distribution,　computational　resources,　and　communication　resources.　This　often　results　in　ineffective　utilization　of　local　updates　from　the　devices　in　each　iteration,　ultimately　leading　to　a　substantial　decline　in　the　convergence　rate　and　speed　of　federated　learning.　To　address　the　variability　in　data　distribution　and　communication　efficiency　among　participating　devices,　a　Clustering-based　Adaptive　Weight　SelectionAggregation　framework　(C-AW)　is　introduced.　This　framework　dynamically　clusters　the　participating　devices,　adjusts　the　relative　weights　between　the　clusters,　and　intelligently　selects　client　devices　to　participate　in　the　subsequent　rounds　of　federated　learning　based　on　the　cluster　weights.　The　effectiveness　of　C-AW　has　been　experimentally　validated.　When　using　deep　neural　networks　on　the　CIFAR-10　dataset,　C-AW　demonstrates　superior　performance　in　both　training　time　and　accuracy　compared　to　the　Federated　Averaging　algorithm　(FEDAVG)　as　well　as　other　selection　and　weighting　learning　methods.　The　experimental　results　confirm　that　the　proposed　C-AW　framework　can　effectively　enhance　the　efficiency　and　accuracy　of　federated　learning　in　heterogeneous　environments.　©　2024　IEEE.