As　an　emerging　sensing　data　collection　paradigm,　mobile　crowdsensing　(MCS)　enjoys　good　scalability　and　low　deployment　cost　but　raises　privacy　concerns.　In　this　paper,　we　propose　a　privacy-preserving　MCS　system　called　CROWDFL　by　seamlessly　integrating　federated　learning　(FL)　into　MCS.　At　a　high　level,　in　order　to　protect　participants＇　privacy　and　fully　explore　participants＇　computing　power,　participants　in　CROWDFL　locally　process　sensing　data　via　FL　paradigm　and　only　upload　encrypted　training　models　to　the　server.　To　this　end,　we　design　a　secure　aggregation　algorithm　(SecAgg)　through　the　threshold　Paillier　cryptosystem　to　aggregate　training　models　in　an　encrypted　form.　Also,　to　stimulate　participation,　we　present　a　hybrid　incentive　mechanism　combining　the　reverse　Vickrey　auction　and　posted　pricing　mechanism,　which　is　proved　to　be　truthful　and　fail.　Results　of　theoretical　analysis　and　experimental　evaluation　on　a　practical　MCS　scenario　(human　activity　recognition)　show　that　CROWDFL　is　effective　in　protecting　participants＇　privacy　and　is　efficient　in　operations.　In　contrast　to　existing　solutions,　CROWDFL　is　3x　faster　in　model　decryption　and　improves　an　order　of　magnitude　in　model　aggregation.