Federated Learning (FL) is a collaborative learning framework that enables edge devices to collaboratively learn a global model while keeping raw data locally. Although FL avoids leaking direct information from local datasets, sensitive information can still be inferred from the shared models. To address the privacy issue in FL, differential privacy (DP) mechanisms are leveraged to provide formal privacy guarantee. However, when deploying FL at the wireless edge with over-the-air computation, ensuring client-level DP faces significant challenges. In this paper, we propose a novel wireless FL scheme called private federated edge learning with sparsification (PFELS) to provide client-level DP guarantee with intrinsic channel noise while reducing communication and energy overhead and improving model accuracy. The key idea of PFELS is for each device to first compress its model update and then adaptively design the transmit power of the compressed model update according to the wireless channel status without any artificial noise addition. We provide a privacy analysis for PFELS and prove the convergence of PFELS under general non-convex and non-IID settings. Experimental results show that compared with prior work, PFELS can improve the accuracy with the same DP guarantee and save communication and energy costs simultaneously.

翻译：联邦学习（FL）是一种协作学习框架，使边缘设备能够在本地保留原始数据的同时协同学习全局模型。尽管联邦学习避免了从本地数据集中泄露直接信息，但敏感信息仍可能从共享模型中推断得出。为解决联邦学习中的隐私问题，差分隐私（DP）机制被用于提供严格的隐私保证。然而，在无线边缘部署带空中计算的联邦学习时，确保客户端级别的差分隐私面临重大挑战。本文提出一种新型无线联邦学习方案——基于稀疏化的私有联邦边缘学习（PFELS），该方案利用内在信道噪声提供客户端级差分隐私保证，同时降低通信与能量开销并提升模型精度。PFELS的核心思想是：每个设备首先压缩其模型更新，随后根据无线信道状态自适应设计压缩模型更新的发射功率，无需添加任何人工噪声。我们为PFELS提供了隐私性分析，并证明了其在一般非凸和非独立同分布（non-IID）条件下的收敛性。实验结果表明，与已有工作相比，PFELS能在相同差分隐私保证下提升精度，同时节约通信与能量成本。