Crowdsensing　has　become　a　popular　method　of　sensing　data　collection　while　facing　the　problem　of　protecting　participants＇　location　privacy.　Existing　location-privacy　crowdsensing　mechanisms　focus　on　static　tasks　and　participants　without　considering　sensing　tasks＇　time　requirements　and　participants＇　mobility,　which　cannot　achieve　satisfactory　collected　data　quality　and　task　completion　in　crowdsensing　with　dynamic　tasks　and　participants.　Inspired　by　this,　we　proposed　a　location-preservation　crowdsensing　mechanism,　FedSense,　considering　dynamic　tasks　and　participants　based　on　federated　learning　(FL)　and　reinforcement　learning　(RL).　In　FedSense,　through　RL＇s　outstanding　decision-making　ability,　participants　select　sensing　tasks　to　perform　by　well-trained　RL　models　without　uploading　location　information　to　servers　for　task　allocation.　We　propose　an　independent　tasks　selection　environment　that　defines　actions,　states,　and　rewards　of　RL　to　enable　FedSense　to　achieve　satisfactory　task　completion　and　data　quality　while　preserving　location　privacy.　Besides,　FedSense　applies　an　asynchronous　FL　aggregation　algorithm　that　reduces　participants＇　network　stabilization　and　device　computing　ability　requirements.　Analysis　proves　that　participants＇　location　information　does　not　leave　the　local　device　during　the　model　training　and　task　selection　process,　effectively　avoiding　privacy　leakage.　Simulation　shows　that　compared　with　existing　location-preservation　crowdsensing　mechanisms,　FedSense　achieves　the　highest　task　completion　and　sensing　accuracy　for　dynamic　tasks　and　participants.