  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.
