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.

翻译：我们提出了一种名为FLORAS的隐私保护上行链路空中计算（AirComp）方法，适用于单输入单输出（SISO）无线联邦学习（FL）系统。从通信设计角度，FLORAS利用正交序列的特性，消除了对发射机信道状态信息（CSIT）的需求。从隐私角度，我们证明FLORAS同时提供条目级和客户端级差分隐私（DP）保证。此外，通过适当调整系统参数，FLORAS能够在无需额外成本的情况下灵活实现不同DP水平。推导出新的FL收敛边界，该边界与隐私保证相结合，可在收敛速率与差分隐私水平之间实现平滑折中。实验结果表明，与基线AirComp方法相比，FLORAS具有优势，并验证了分析结果可指导在不同模型收敛性与隐私水平折中需求下设计隐私保护FL。