Although　federated　learning　can　provide　privacy　protection　for　individual　raw　data,　some　studies　have　shown　that　the　shared　parameters　or　gradients　under　federated　learning　may　still　reveal　user　privacy.　Differential　privacy　is　a　promising　solution　to　the　above　problem　due　to　its　small　computational　overhead.　At　present,　differential　privacy-based　federated　learning　generally　focuses　on　the　trade-off　between　privacy　and　model　convergence.　Even　though　differential　privacy　obscures　sensitive　information　by　adding　a　controlled　amount　of　noise　to　the　confidential　data,　it　opens　a　new　door　for　model　poisoning　attacks:　attackers　can　use　noise　to　escape　anomaly　detection.　In　this　paper,　we　propose　a　novel　model　poisoning　attack　called　Model　Shuffle　Attack　(MSA),　which　designs　a　unique　way　to　shuffle　and　scale　the　model　parameters.　If　we　treat　the　model　as　a　black　box,　it　behaves　like　a　benign　model　on　test　set.　Unlike　other　model　poisoning　attacks,　the　malicious　model　after　MSA　has　high　accuracy　on　test　set　while　reducing　the　global　model　convergence　speed　and　even　causing　the　model　to　diverge.　Extensive　experiments　show　that　under　FedAvg　and　robust　aggregation　rules,　MSA　is　able　to　significantly　degrade　performance　of　the　global　model　while　guaranteeing　stealthiness.