With　the　development　of　deep　learning,　people　are　more　and　more　concerned　about　the　security　of　data.　Federated　learning　can　solve　the　problem　of　data　island,　but　it　also　brings　more　serious　data　privacy　problems.　Furthermore,　in　the　process　of　multisource　data　collaboration,　the　efficiency　of　the　whole　federated　learning　system　is　usually　not　high.　In　this　article,　we　introduce　a　scheme　named　FVFL,　which　ensure　the　local　data　security　and　resistance　to　collusive　attacks,　more　importantly　it　can　well　support　client　flexible　participate　federated　learning.　We　adopt　Paillier　encryption　and　secret　sharing　to　guarantee　client＇s　data　security　and　resistance　to　collusive　attacks.　Moreover,　our　encryption　mechanism　allows　client　to　participate　in　federated　learning　flexibly,　and　the　correctness　of　the　encryption　algorithm　is　not　affected　by　client＇s　drop　out.　The　super-increasing　sequence　is　introduced　to　reduce　the　communication　overhead　of　the　whole　system,　the　simulation　result　shows　that　the　result　is　significant;　the　Lagrange　interpolation　polynomial　and　secret　Sharing　is　introduced　to　implement　verification　mechanism,　to　prevent　malicious　forgery　of　aggregation　results　in　the　cloud.　The　verification　mechanism　ensures　the　clients　to　obtain　real　and　reliable　aggregation　results　in　the　cloud.　Moreover,　our　verification　mechanism　allows　client　to　participate　in　federated　learning　flexibly,　and　the　correctness　of　the　verification　algorithm　is　not　affected　by　client＇s　drop　out.　And　the　experimental　results　show　that　FVFL　has　high　accuracy　and　efficiency.　©　2014　IEEE.