Debiased Device Sampling for Federated Edge Learning in Wireless Networks

As a privacy-preserved distributed machine learning paradigm, federated edge learning (FEL) was designed to absorb knowledge from user devices to construct intelligent services without transmitting raw data. However, this paradigm depends on the local training and model parameter transmission of user devices, therefore the computing power, storage capacity and network resources of the devices become the key factors to achieve energy well-budgeted and timely message transmission FEL. While in the wireless networks, those resources for devices are normally heterogeneous or limited. This paper aims to offer tangible solutions for optimal convergence and Quality of Service (QoS) assurance of FEL in wireless networks. First, we define a mathematical model for energy-efficient message transmission of FEL and formulate an optimization problem involving device sampling and resource allocation to attain optimal training convergence within energy and time constraints. Second, we theoretically analyze the impact of limited resources on sampling strategies and training convergence, thus simplifying the optimization problem for solvability. Third, we introduce an iterative heuristic algorithm that utilizes available resources to reduce client sampling bias. Extensive experiments show that our method can effectively obtain the debiased sampling strategy, and outperforms similar methods by minimizing device disconnection due to energy use and enhancing model convergence and performance.