Mobile　crowd　sensing　uses　the　combined　effects　of　a　large　number　of　users　to　collect,　process,　and　reuse　data　for　different　types　of　applications.　Federated　Learning　enables　training　a　global　model　without　compromising　users’　privacy.　In　this　work,　we　attempt　to　explore　a　new　distributed　sensing　and　learning　paradigm,　called　Federated　Crowd　Sensing　(FCS).　Specifically,　we　propose　a　two-tiered　incentive　mechanism　for　FCS.　First,　we　design　an　incentive　mechanism　in　the　participant　recruitment　stage　where　we　consider　the　heterogeneous　network　effect,　where　larger　fraction　of　participants　will　give　potential　mobile　users　an　added　value,　but　different　users　perceive　it　differently.　Second,　we　design　another　incentive　mechanism　in　the　task　result　collection　stage　where　we　propose　a　hybrid　uploading　strategy　selected　by　the　users　after　completing　the　FCS　tasks.　Using　the　proposed　algorithm　for　optimal　uploading　mechanism,　the　participants　can　increase　their　own　utility.　The　numerical　results　show　that　platform　can　attract　more　potential　mobile　users,　gain　higher　utility　for　platform　and　participants,　and　reduce　the　overall　cost.　©　2022,　China　Computer　Federation　(CCF).