Mobile crowdsensing (MCS) combined with federated learning, as an emerging data collection and intelligent process paradigm, has received lots of attention in social networks and mobile Internet-of-Things, etc. However, as the openness and transparent of mobile crowdsensing tasks, federated learning model and training samples for crowdsensing data still face enormous privacy revealing risks, and it will reduce the willingness of people or nodes to actively participate and provide data in MCS. In this paper, we present a Privacy-Enhanced Aggregation for Federated Learning in MCS, namely PrivacyEAFL, to implement the training of federated learning under mobile crowdsensing system in terms of privacy protection of all participants. Firstly, considering that the crowdsensing server might share information with some participants to obtain and leak some local models, we design a collusion-resistant data aggregation approach by combining homomorphic cryptosystem and hashed Diffie-Hellman key exchange protocol. Secondly, we design a data encoding and aggregating method with data packing which can reduce the computation cost and communication overhead for the system. Thirdly, as the number of participants’ samples are dynamically changeable in MCS, we design a sample number protection method that can implement the security and privacy of the number of training samples owned by participants. Finally, we provide the experimental results on real-world datasets (i.e, MNIST and Car Evaluation) with crowdsensing devices underRaspberry-Pi 4BandRedmi-K30 Pro, respectively, and the results demonstrate that our scheme is more efficient and practical in secure and privacy-enhanced model aggregation for federated learning in mobile crowdsensing.

This letter focuses on adaptive target detection in the presence of multiple interference sources, which comprise clutter, thermal noise, noise-like jammers, and fully-correlated (or coherent) signals. In order to account for different operating scenarios, we formulate the problem at hand in terms of a multiple hypothesis test with several alternative hypotheses representative of each considered scenario. In this context, we devise a family of two-stage detection architectures capable of classifying the specific scenario and, hence, of working under different operating conditions. The performance analysis shows the effectiveness of the detector based upon the Generalized Information Criterion also in comparison with traditional adaptive decision schemes.