  In conventional federated learning (FL), differential privacy (DP) guarantees
can be obtained by injecting additional noise to local model updates before
transmitting to the parameter server (PS). In the wireless FL scenario, we show
that the privacy of the system can be boosted by exploiting over-the-air
computation (OAC) and anonymizing the transmitting devices. In OAC, devices
transmit their model updates simultaneously and in an uncoded fashion,
resulting in a much more efficient use of the available spectrum. We further
exploit OAC to provide anonymity for the transmitting devices. The proposed
approach improves the performance of private wireless FL by reducing the amount
of noise that must be injected.
