In　order　to　solve　the　problems　of　distributed　backdoor　attack　under　federated　learning,　a　distributed　backdoor　attack　defense　scheme　(DAGUARD)　under　federated　learning　was　proposed　based　on　the　assumption　that　the　server　selected　no　more　than　half　of　malicious　clients　for　global　aggregation.　The　partial　update　strategy　of　the　triple　gradient　optimization　algorithm　(TernGrad)　was　designed　to　solve　the　backdoor　attack　and　inference　attack,　an　adaptive　density　clustering　defense　scheme　was　designed　to　solve　the　backdoor　attacks　with　relatively　large　angle　deflection,　the　adaptive　clipping　scheme　was　designed　to　limit　the　enhancement　backdoor　attack　that　amplify　the　gradients　and　the　adaptive　noise-enhancing　scheme　was　designed　to　weaken　distributed　backdoor　attacks.　The　experimental　results　show　that　in　the　federated　learning　scenario,　the　proposed　scheme　has　better　defense　performance　and　defense　stability　than　existing　defense　strategies.　©　2023　Editorial　Board　of　Journal　on　Communications.　All　rights　reserved.