In　the　era　of　data-driven　artificial　intelligence,　the　widespread　deployment　of　IoT　devices　has　amplified　concerns　around　privacy　and　data　security.　Federated　learning　(FL)　offers　a　promising　solution　by　enabling　local　model　training　without　exposing　raw　data,　effectively　mitigating　privacy　risks.　However,　the　inherent　heterogeneity　of　IoT　data　leads　to　significant　disparities　in　data　distributions　across　different　clients,　negatively　impacting　the　global　model＇s　performance.　Furthermore,　conventional　fixed　differential　privacy　mechanisms　lack　the　adaptability　needed　to　dynamically　adjust　to　the　evolving　requirements　of　different　training　phases,　limiting　their　effectiveness　in　privacy-preserving　federated　learning.　To　address　these　challenges,　we　propose　a　federated　learning　framework　called　AWE-DPFL,　which　integrates　adaptive　weight　fusion　and　dynamic　privacy　budget　adjustment　mechanisms.　AWE-DPFL　employs　a　dynamic　privacy　budget　adjustment　strategy　to　allocate　privacy　budgets　based　on　the　variance　in　client　model　updates,　thereby　improving　model　performance　while　ensuring　robust　privacy　protection.　Additionally,　the　adaptive　weight　fusion　mechanism　assigns　different　weights　to　each　client＇s　model,　taking　into　account　data　heterogeneity　and　quality,　which　leads　to　an　enhanced　global　model　that　better　reflects　individual　client　contributions.　Moreover,　AWE-DPFL　incorporates　meta-learning　alongside　differential　privacy　techniques　during　local　model　training,　resulting　in　an　effective　balance　between　data　privacy　and　model　performance.　This　approach　not　only　improves　model　adaptability　and　generalization　across　diverse　data　distributions　but　also　ensures　that　privacy　requirements　are　met　throughout　the　training　process.　Experimental　evaluations　demonstrate　that　AWE-DPFL　significantly　outperforms　existing　approaches　on　the　MNIST,　FashionMNIST,　HAR,　and　Edge-IIoTset　datasets,　showcasing　its　effectiveness　as　a　federated　learning　solution　for　real-world　IoT　applications.