Federated　learning　has　gained　popularity　as　it　enables　collaborative　training　without　sharing　local　data.　Despite　its　advantages,　federated　learning　requires　sharing　the　model　parameters　during　model　aggregation　which　poses　security　risks.　In　addition,　existing　secure　federated　learning　frameworks　cannot　meet　all　the　requirements　of　resource-constrained　IoT　devices　and　non-independent　and　identically　distributed　(non-IID)　setting.　This　paper　proposes　a　novel　secure　and　robust　federated　learning　framework　(SRFL)　with　trusted　execution　environments　(TEEs).　The　framework　provides　security　and　robustness　for　federated　learning　on　IoT　devices　under　non-IID　data　by　leveraging　TEEs　to　safeguard　sensitive　model　components　from　being　leaked.　Simultaneously,　we　introduce　a　shared　representation　training　approach　to　enhance　the　accuracy　and　security　under　non-IID　setting.　Furthermore,　a　multi-model　robust　aggregation　method　using　membership　degree　is　proposed　to　enhance　robustness.　This　method　uses　membership　degree　generated　by　soft　clustering　to　categorize　clients　for　better　aggregation　performance.　Additionally,　we　evaluate　SRFL　in　a　simulation　environment,　confirming　that　it　improves　accuracy　by　5%-30%　over　FedAVG　in　non-IID　setting　and　protects　the　model　from　membership　inference　attack　and　Byzantine　attack.　It　also　reduces　backdoor　attack　success　rate　by　4%-10%　more　compared　to　other　robust　aggregation　algorithms.