Over-the-Air Aggregation-Based Federated Learning in Cache-Enabled Wireless Edge Networks

Jun-Pyo Hong, Sangjun Park, Wan Choi

Abstract: In conventional federated learning (FL), dataset at the parameter server (PS) is not usually considered but may enhance the performance of FL if available. The benefit of leveraging the dataset at PS can be multiplied in over-the-air aggregation-based FL where combating local gradient update distortion against channel fading is inordinately consequential. In this paper, an over-the-air aggregation framework for communication efficient FL is investigated in cache-enabled wireless edge networks where not only edge devices but also a base station (BS) has its own local dataset. The proposed framework leverages the BS dataset to reduce the number of channel uses necessary for the model convergence and to avoid the overhead incurred by power scale coordination and global channel state information (CSI) acquisition at BS. We present a sufficient condition for convergence to a stationary point without convexity assumption on the objective function. Based on the sufficient condition, a power control method is optimized to facilitate the model convergence without assumptions on power scale coordination and global CSI at BS. Our simulation results validate that BS dataset is beneficial to reduce the number of channel uses for the model convergence and the developed power control method outperforms the conventional method in terms of both convergence rate and converged test accuracy.