A Demonstration of Over-the-Air Computation for Federated Edge Learning
This work addresses synchronization challenges in federated edge learning for edge computing applications, but it is incremental as it builds on existing over-the-air computation methods.
The study tackled the problem of synchronizing software-defined radios for over-the-air computation in federated edge learning, achieving over 95% test accuracy for both homogeneous and heterogeneous data distributions without requiring channel state information at edge devices.
In this study, we propose a general-purpose synchronization method that allows a set of software-defined radios (SDRs) to transmit or receive any in-phase/quadrature data with precise timings while maintaining the baseband processing in the corresponding companion computers. The proposed method relies on the detection of a synchronization waveform in both receive and transmit directions and controlling the direct memory access blocks jointly with the processing system. By implementing this synchronization method on a set of low-cost SDRs, we demonstrate the performance of frequency-shift keying (FSK)-based majority vote (MV), i.e., an over-the-air computation scheme for federated edge learning, and introduce the corresponding procedures. Our experiment shows that the test accuracy can reach more than 95% for homogeneous and heterogeneous data distributions without using channel state information at the edge devices.