  We propose a privacy-preserving uplink over-the-air computation (AirComp)
method, termed FLORAS, for single-input single-output (SISO) wireless federated
learning (FL) systems. From the perspective of communication designs, FLORAS
eliminates the requirement of channel state information at the transmitters
(CSIT) by leveraging the properties of orthogonal sequences. From the privacy
perspective, we prove that FLORAS offers both item-level and client-level
differential privacy (DP) guarantees. Moreover, by properly adjusting the
system parameters, FLORAS can flexibly achieve different DP levels at no
additional cost. A new FL convergence bound is derived which, combined with the
privacy guarantees, allows for a smooth tradeoff between the achieved
convergence rate and differential privacy levels. Experimental results
demonstrate the advantages of FLORAS compared with the baseline AirComp method,
and validate that the analytical results can guide the design of
privacy-preserving FL with different tradeoff requirements on the model
convergence and privacy levels.
