# Personalizing Federated Learning with Over-the-Air Computations

## TL;DR

This paper introduces a personalized federated learning framework using over-the-air computation to improve training efficiency and model robustness in wireless edge networks, addressing communication and data heterogeneity challenges.

## Contribution

It proposes a novel combination of analog over-the-air computation with bi-level optimization for personalized federated learning, enhancing efficiency and robustness.

## Key findings

- Improved training efficiency demonstrated through convergence analysis.
- Enhanced model personalization and robustness validated by extensive experiments.
- Effective handling of communication bottlenecks in wireless networks.

## Abstract

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.