Differentially Private Wireless Federated Learning Using Orthogonal Sequences

We propose a privacy-preserving uplink overthe- air computation (AirComp) method, termed FLORAS, for single-input single-output (SISO) wireless federated learning (FL) systems. From the perspective of communication designs, FLORASeliminates the requirement of channel state information at the transmitters (CSIT) by leveraging the properties of orthogonal sequences. From the privacy perspective, we prove that FLORASoffers both item-level and client-level differential privacy (DP) guarantees. Moreover, by properly adjusting the system parameters, FLORAScan 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 FLORAScompared with the baseline AirComp method, and validate that the analytical results can guide the design of privacypreserving FL with different tradeoff requirements on the model convergence and privacy levels.