Federated edge learning is a promising technology to deploy intelligence at the edge of wireless networks in a privacy-preserving manner. Under such a setting, multiple clients collaboratively train a global generic model under the coordination of an edge server. But the training efficiency is often throttled by challenges arising from limited communication and data heterogeneity. This paper presents a distributed training paradigm that employs analog over-the-air computation to address the communication bottleneck. Additionally, we leverage a bi-level optimization framework to personalize the federated learning model so as to cope with the data heterogeneity issue. As a result, it enhances the generalization and robustness of each client's local model. We elaborate on the model training procedure and its advantages over conventional frameworks. We provide a convergence analysis that theoretically demonstrates the training efficiency. We also conduct extensive experiments to validate the efficacy of the proposed framework.

翻译：联邦边缘学习是一种在无线网络边缘以隐私保护方式部署智能的有前景技术。在该场景下，多个客户端在边缘服务器的协调下协同训练一个全局通用模型。然而，训练效率常受限于通信瓶颈和数据异构性带来的挑战。本文提出了一种分布式训练范式，采用模拟空中计算来解决通信瓶颈问题。此外，我们利用双层优化框架对联邦学习模型进行个性化，以应对数据异构性问题，从而增强每个客户端本地模型的泛化能力和鲁棒性。我们详细阐述了模型训练过程及其相对于传统框架的优势，并提供了收敛性分析以从理论上证明训练效率。我们还进行了广泛实验以验证所提框架的有效性。