CrowdFL: Privacy-Preserving Mobile Crowdsensing System Via Federated Learning

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 3× faster in model decryption and improves an order of magnitude in model aggregation.