FedSky: An Efficient and Privacy-preserving Scheme for Federated Mobile Crowdsensing

Mobile Crowdsensing (MCS) is a newly-emerged sensing paradigm where a large group of mobile workers collectively sense and share data for real-time services. However, one major problem that hinders the further development of MCS is the potential leakage of workers’ data privacy. In this paper, we integrate Federated Learning (FL) with MCS and introduce a novel sensing system called Federated Mobile Crowdsensing (F-MCS). In F-MCS, the workers can optimize the global model while keeping all the sensitive training data locally, thus ensuring their data privacy. Nevertheless, there are still two major issues in F-MCS. The first issue is that in F-MCS services, the workers are heterogeneous in terms of computational capacities and data resources. Hence, qualified workers should be appropriately selected to improve the efficiency of the training process. The second issue is that F-MCS is a cross-device FL system where the platform will finally get the global model after multiple training rounds. However, most privacy-preserving techniques are designed for cross-silo FL platforms, which cannot be applied to real-world F-MCS scenarios. To tackle the above problems, in this paper, we propose a privacy-preserving scheme for F-MCS, namely FedSky. Mainly, by extending the classic FedAvg algorithm, FedSky selects qualified workers based on constrained group skyline (CG-skyline) and securely aggregates model updates based on the homomorphic encryption technique. Comprehensive security analysis demonstrates the privacy-preservation of FedSky. Extensive experiments are conducted on an image classification task, where the comparison results validate the proposed scheme’s efficiency and effectiveness.