The　heterogeneous　cluster　networks　(HCN)　have　recently　benefited　from　federated　learning　(FL).　On　distributed　data,　FL　is　used　to　train　privacy-preserving　models.　In　heterogeneous　networks　(HetNet)　and　the　Internet　of　Things　(IoT),　FL　implementation　is　challenged　by　resource　optimization,　robustness,　and　security　issues.　There　is　a　significant　risk　of　data　security　being　compromised　by　disregarding　the　nodes＇　clustering　behavior　and　quickly　varying　asynchronous　streaming　data.　Moreover,　in　HCN-based　wireless　sensor　networks　(WSNs),　FL　enhances　asynchronous　node　performance.　Using　naturally　clustered　HCN,　distributed　nodes　train　a　local　and　global　model　collectively.　In　this　paper,　we　propose　a　Intra-Clustered　FL　(ICFL)　model.　By　optimizing　computation　and　communication,　ICFL　selects　heterogeneous　FL　nodes　in　each　cluster.　Despite　heterogeneous　data,　it　is　highly　robust.　There　are　currently　no　FL　frameworks　that　can　handle　varying　data　quality　across　devices　and　non-identical　distributions.　With　ICFL,　sensitive　asynchronous　data　is　not　exposed　to　possible　misuse　while　adapting　to　changing　environments.　In　addition　to　being　time-efficient,　our　strategy　requires　low-power　computing　nodes.　According　to　our　extensive　simulation　results,　ICFL　performs　better　than　FedCH　in　terms　of　computational　performance　and　provides　flexible　conditions　under　which　ICFL　is　more　efficient　in　terms　of　communication.　In　extensive　testing,　ICFL　decreased　training　rounds　by　62%　and　increased　accuracy　by　6.5%.　It　can　execute　evaluations　7.46　times　faster　than　existing　models,　and　its　average　accuracy　has　increased　by　4.39%.　A　resource-aware　FL　system　can　be　successfully　implemented　in　real-time　applications　according　to　our　research.