Federated　Learning,　as　a　typical　paradigm　of　collaborative　learning,　effectively　mediates　the　contradictions　between　model　training　and　data　privacy.　However,　the　data　heterogeneity　and　privacy　leakage　problems　undermine　the　availability　of　federated　learning.　Existing　studies　focus　solely　on　one　of　the　problems.　In　this　work,　we　manage　to　handle　these　two　challenges　simultaneously,　by　utilizing　contrastive　learning　and　differential　privacy　in　local　training,　the　federated　learning　system　can　stay　safe　and　robust.　The　contrastive　loss　item　pushes　the　local　model　towards　the　global　model　and　a　random　noise　generated　by　differential　privacy　is　added　to　the　model　to　protect　the　sensitive　information　hidden　in　the　models.　Extensive　experiment　results　demonstrate　that　our　method　can　exceed　the　baseline　around　1%2%　in　term　of　test　accuracy.　　©　2024　IEEE.