In　recent　years,　federated　learning(FL)　has　made　significant　progress,　with　a　primary　focus　on　privacy　protection,　model　optimization,　and　enhancing　system　security　and　robustness.　Federated　Learning　can　be　categorized　into　two　main　types:　centralized　federated　learning(CFL)　and　decentralized　federated　learning(DFL).　Traditional　CFL　methods　rely　on　a　central　server　to　aggregate　model　updates　from　clients,　while　DFL,　as　an　improvement　over　CFL,　aims　to　directly　exchange　model　updates　between　clients　in　a　peer-to-peer　manner,　thereby　eliminating　the　dependence　on　a　central　server　and　reducing　communication　burdens.　In　this　work,　we　built　a　deep　neural　network　model　within　the　context　of　DFL,　and　considering　the　issue　of　data　heterogeneity　often　arising　in　DFL,　we　further　proposed　the　comprehensive　factors　for　neighbor　selection(CFNS)　method.　This　method　selects　neighboring　clients　with　similar　data　distributions　by　considering　multiple　factors,　with　the　assistance　of　a　graph　structure　in　Federated　Learning.　Moreover,　although　DFL　can　reduce　risks　such　as　single-point　privacy　breaches　to　some　extent,　privacy　protection　still　requires　further　attention.　To　address　this,　we　introduced　a　local　differential　privacy(LDP)　module　during　client　communication,　further　enhancing　DFL＇s　privacy　protection　capabilities.　Experimental　results　show　that　our　method　can　achieve　good　performance　while　ensuring　privacy　security,　and　the　results　outperform　the　provided　baseline　methods.　©　2024　Copyright　held　by　the　owner/author(s).　Publication　rights　licensed　to　ACM.