We propose a novel privacy-preserving uplink over-the-air computation (AirComp) method, termed FLORAS, for single-input single-output (SISO) wireless federated learning (FL) systems. From the communication design perspective, 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 can offer both item-level and client-level differential privacy (DP) guarantees. Moreover, by adjusting the system parameters, FLORAS can flexibly achieve different DP levels at no additional cost. A novel FL convergence bound is derived which, combined with the privacy guarantees, allows for a smooth tradeoff between convergence rate and differential privacy levels. Numerical results demonstrate the advantages of FLORAS compared with the baseline AirComp method, and validate that our analytical results can guide the design of privacy-preserving FL with different tradeoff requirements on the model convergence and privacy levels.

通过使用正交序列的特性, FLORAS在通信设计角度消除了发送器处的信道状态信息(CSIT)的要求,提出了一种用于单输入单输出(SISO)无线联邦学习(FL)系统的新颖的隐私保护上行计算(Over-the-Air Computation)方法, 并通过调整系统参数,可以在没有额外成本的情况下灵活地实现不同的差分隐私(DP)级别。同时,FLORAS提供了单项和客户差分隐私保证。

使用正交序列的差分隐私无线联邦学习