Federated　learning　is　widely　used　and　researched　as　an　effective　method　for　solving　the　privacy　problems　faced　by　centralized　learning.　To　address　the　communication　limitations　and　heterogeneity　among　clients,　many　existing　methods　based　on　the　mixup　algorithm　share　data　mixed　with　the　client＇s　local　dataset　to　improve　the　model　accuracy.　However,　due　to　the　heterogeneity　of　federated　learning,　there　may　be　some　clients　who　join　the　mixup　process　with　insufficient　data,　which　will　violate　the　privacy-preserving　assumption　of　the　mixup.　Because　of　this　weakness,　many　methods　based　on　the　mixup　approach　will　face　a　serious　privacy　problem　while　trying　to　improve　federated　learning　over　other　parts,　e.g.,　accuracy　or　communication　efficiency.　Therefore,　we　propose　the　FedMDO　framework　to　solve　the　privacy　problem　faced　by　mixup-based　methods.　In　FedMDO,　we　introduce　the　auxiliary　client　to　hold　the　auxiliary　dataset　that　is　related　to　the　federated　learning　task　and　to　generate　the　mixup　templates　for　clients　to　increase　the　amount　of　data　in　the　mixup　process.　By　introducing　the　auxiliary　client,　the　decrease　in　model　accuracy　can　be　suppressed　as　much　as　possible　while　taking　advantage　of　the　privacy-preserving　gain　from　the　increase　in　data　volume.　Furthermore,　we　introduce　differential　privacy　into　FedMDO　with　an　elaborate　redesign　to　enhance　privacy　protection.　The　corresponding　analysis　shows　that　under　FedMDO,　differential　privacy　can　achieve　the　same　protection　with　less　negative　impact.　Experiments　show　that　with　at　least　an　approximately　10%+　improvement　in　model　accuracy　and　an　average　of　5　times　greater　communication　savings　compared　to　the　FedAvg　and　non-IID　SOTAs　with　weak　privacy　protection　design,　our　method　can　yield　significant　improvement　in　the　privacy　of　the　shared　data.　©　1991-2012　IEEE.