Federated　learning　is　emerged　as　an　attractive　paradigm　regarding　the　data　privacy　problem,　clients　train　the　deep　neural　network　on　their　local　datasets,　there　is　no　need　to　upload　their　local　data　to　a　center　server,　and　gradients　are　shared　instead.　However,　recent　studies　show　that　adversaries　can　reconstruct　the　training　images　at　high　resolution　from　the　gradients,　such　a　break　of　data　privacy　is　possible　even　in　trained　deep　networks.　To　protect　data　privacy,　a　secure　aggregation　scheme　against　inverting　attack　is　proposed　for　federated　learning.　The　gradients　are　encrypted　before　sharing,　and　an　adversary　is　unable　to　launch　various　attacks　based　on　gradients.　To　improve　the　efficiency　of　data　aggregation　schemes,　a　new　way　of　building　shared　keys　is　proposed,　and　a　client　build　shared　keys　with　2a　other　clients,　but　not　all　the　clients　in　the　system.　Besides,　the　gradient　inversion　attacks　are　also　tested,　and　a　gradient　inversion　attack　is　proposed,　which　enable　the　adversary　to　reconstruct　the　training　data　based　on　gradient.　The　simulation　results　show　the　proposed　scheme　can　protect　an　honest　but　curious　parameter　server　from　reconstructing　the　training　data.