Federated　Learning　allows　multiple　clients　to　train　local　models　and　aggregate　them　on　the　server　side.　The　client　is　invisible　to　the　shared　global　model　generated　by　the　server,　which　provides　an　opportunity　for　malicious　attackers　to　utilize　the　inherent　vulnerability　of　federated　learning　to　initiate　data　leakage　attacks.　Existing　attack　techniques　are　largely　client-based　and　focus　on　inferring　model　parameters　directly,　but　do　not　work　for　server-based　attacks,　mainly　due　to　differences　in　their　ability　to　generalize　attacks.　Yet　few　robust　data　leakage　attacks　toward　federated　learning　vulnerability　have　been　developed　on　the　server　side.　To　address　the　above　problem,　we　propose　MOFDRNet,　a　Multi-Objective　Fake　Data　Regression　Network　model　that　integrates　the　loss　function　and　multiple　metrics　strategies.　The　key　idea　is　to　deploy　a　malicious　attack　model　on　the　server　with　the　purpose　of　generating　fake　data　and　labels　and　continuously　approximating　the　shared　gradients　between　clients　and　the　server,　thereby　recovering　clients＇　private　data.　Experimental　results　demonstrate　that　the　MOFDRNet　model　has　significant　advantages　in　implementing　data　leakage　attacks.　Finally,　we　also　discuss　the　differential　privacy　defense　approach　in　this　study.　©　2025　John　Wiley　&　Sons　Ltd.