In　the　realm　of　the　Internet　of　Things　(IoT),　deploying　deep　learning　models　to　process　data　generated　or　collected　by　IoT　devices　is　a　critical　challenge.　However,　direct　data　transmission　can　cause　network　congestion　and　inefficient　execution,　given　that　IoT　devices　typically　lack　computation　and　communication　capabilities.　Centralized　data　processing　in　data　centers　is　also　no　longer　feasible　due　to　concerns　over　data　privacy　and　security.　To　address　these　challenges,　we　present　an　innovative　Edge-assisted　U-Shaped　Split　Federated　Learning　(EUSFL)　framework,　which　harnesses　the　high-performance　capabilities　of　edge　servers　to　assist　IoT　devices　in　model　training　and　optimization　process.　In　this　framework,　we　leverage　Federated　Learning　(FL)　to　enable　data　holders　to　collaboratively　train　models　without　sharing　their　data,　thereby　enhancing　data　privacy　protection　by　transmitting　only　model　parameters.　Additionally,　inspired　by　Split　Learning　(SL),　we　split　the　neural　network　into　three　parts　using　U-shaped　splitting　for　local　training　on　IoT　devices.　By　exploiting　the　greater　computation　capability　of　edge　servers,　our　framework　effectively　reduces　overall　training　time　and　allows　IoT　devices　with　varying　capabilities　to　perform　training　tasks　efficiently.　Furthermore,　we　proposed　a　novel　noise　mechanism　called　LabelDP　to　ensure　that　data　features　and　labels　can　securely　resist　reconstruction　attacks,　eliminating　the　risk　of　privacy　leakage.　Our　theoretical　analysis　and　experimental　results　demonstrate　that　EUSFL　can　be　integrated　with　various　aggregation　algorithms,　maintaining　good　performance　across　different　computing　capabilities　of　IoT　devices,　and　significantly　reducing　training　time　and　local　computation　overhead.