  Federated learning (FL) has emerged as a promising learning paradigm in which
only local model parameters (gradients) are shared. Private user data never
leaves the local devices thus preserving data privacy. However, recent research
has shown that even when local data is never shared by a user, exchanging model
parameters without protection can also leak private information. Moreover, in
wireless systems, the frequent transmission of model parameters can cause
tremendous bandwidth consumption and network congestion when the model is
large. To address this problem, we propose a new FL framework with efficient
over-the-air parameter aggregation and strong privacy protection of both user
data and models. We achieve this by introducing pairwise cancellable random
artificial noises (PCR-ANs) on end devices. As compared to existing
over-the-air computation (AirComp) based FL schemes, our design provides
stronger privacy protection. We analytically show the secrecy capacity and the
convergence rate of the proposed wireless FL aggregation algorithm.
