Data and Channel-Adaptive Sensor Scheduling for Federated Edge Learning via Over-the-Air Gradient Aggregation

Abstract

Over-the-air gradient aggregation and data-aware scheduling have recently drawn great attention due to the outstanding performance in improving communication efficiency for federated edge learning applications. However, in this case, the estimated gradient suffers from the channel and data distortion induced by channel fading and data-aware scheduling, which introduces significant bias and harms the training performance. To solve these problems, we propose a dynamic data and channel adaptive sensor scheduling and power control algorithm combining a residual feedback mechanism. Instead of discarding the gradients not transmitted to the central server, each sensor keeps track of a local residual to store these gradients. Furthermore, by connecting the model update iterations to a dynamic evolution process, we utilize the Lyapunov drift optimization method to analyze the relationship between the training gain and resource allocation. The derived decentralized optimal solution is adaptive to both the channel state information and data importance to seize good transmission opportunity and important gradients. Theoretical analysis is provided on the convergence of the proposed algorithm in practical training scenarios. Simulation results further illustrate that under the same power cost, the proposed scheme has a much faster convergence rate and lower training loss compared to existing baselines.