UAV-Enabled Covert Federated Learning

Integrating unmanned aerial vehicles (UAVs) with federated learning (FL) has been seen as a promising paradigm for dealing with the massive amounts of data generated by intelligent devices. Nevertheless, although FL has natural advantages in data security protection, eavesdroppers can also deduce the raw data according to the shared parameters. Existing works mainly focused on encrypting the content of uploaded parameters, but we believe that it can improve security further by hiding the presence of parameter updating. Therefore, in this paper, we conceive a UAV-enabled covert federated learning architecture, where the UAV is not only responsible for orchestrating the operation of FL but also for emitting artificial noise (AN) to interfere with the eavesdropping of unintended users. To strike a balance between the security level and the training cost (including time overhead and energy consumption), we propose a distributed proximal policy optimization-based strategy for the sake of jointly optimizing the trajectory and AN transmitting power of the UAV, the CPU frequency, the transmitting power and the bandwidth allocation of the participated devices, as well as the needed accuracy of the local model. Furthermore, a series of experiments have been conducted to validate the effectiveness of our proposed scheme.