Federated　Learning　(FL),　a　secure　and　emerging　distributed　learning　paradigm,　has　garnered　significant　interest　in　the　Internet　of　Things　(IoT)　domain.　However,　it　remains　vulnerable　to　adversaries　who　may　compromise　privacy　and　integrity.　Previous　studies　on　privacy-preserving　FL　(PPFL)　have　demonstrated　limitations　in　client　model　personalization　and　resistance　to　poisoning　attacks,　including　Byzantine　and　backdoor　attacks.　In　response,　we　propose　a　novel　PPFL　framework,　FedRectify,　that　employs　a　personalized　dual-layer　approach　through　the　deployment　of　Trusted　Execution　Environments　and　an　interactive　training　strategy.　This　strategy　facilitates　the　learning　of　personalized　client　features　via　private　and　shared　layers.　Furthermore,　to　improve　model＇s　robustness　to　poisoning　attacks,　we　introduce　a　novel　aggregation　method　that　employs　clustering　to　filter　out　outlier　model　parameters　and　robust　regression　to　assess　the　confidence　of　cluster　members,　thereby　rectifying　poisoned　parameters.　We　theoretically　prove　the　convergence　of　FedRectify　and　empirically　validate　its　performance　through　extensive　experiments.　The　results　demonstrate　that　FedRectify　converges　1.47-2.63　times　faster　than　state-of-the-art　methods　when　countering　Byzantine　attacks.　Moreover,　it　can　rapidly　reduce　the　attack　success　rate　to　a　low　level　between　10%　and　40%　in　subsequent　rounds　when　confronting　bursty　backdoor　attacks.