Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation

Understand

Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed learning at the devices and model aggregation in the central server.

• In order to enable efficient wireless data aggregation, over-the-air computation (AirComp) has recently been proposed and attracted immediate attention.
• However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme.
• To combat this effect, the concept of dynamic learning rate (DLR) is proposed in this work.