Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory

Understand

Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients' private data.

• Driven by the ever increasing demand for model training of mobile applications or devices, a vast majority of FL tasks are implemented over wireless fading channels.
• Due to the time-varying nature of wireless channels, however, random delay occurs in both the uplink and downlink transmissions of FL.
• How to analyze the overall time consumption of a wireless FL task, or more specifically, a FL's delay distribution, becomes a challenging but important open problem, especially for delay-sensitive model training.