Energy-Efficient Radio Resource Allocation for Federated Edge Learning

Edge machine learning involves the development of learning algorithms at the network edge to leverage massive distributed data and computation resources. Among others, the framework of federated edge learning (FEEL) is particularly promising for its data-privacy preservation. FEEL coordinates global model training at a server and local model training at edge devices over wireless links. In this work, we explore the new direction of energy-efficient radio resource management (RRM) for FEEL. To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling. They adapt to devices' channel states and computation capacities so as to reduce their sum energy consumption while warranting learning performance. In contrast with the traditional rate-maximization designs, the derived optimal policies allocate more bandwidth to those scheduled devices with weaker channels or poorer computation capacities, which are the bottlenecks of synchronized model updates in FEEL. On the other hand, the scheduling priority function derived in closed form gives preferences to devices with better channels and computation capacities. Substantial energy reduction contributed by the proposed strategies is demonstrated in learning experiments.

I. Introduction

Recent years have witnessed a phenomenal growth in mobile data, most of which are generated in real-time and distributed at edge devices (e.g., smartphones and sensors) [1]. Uploading these massive data to the cloud for training artificial intelligence (AI) models is impractical due to various issues including privacy, network congestion, and latency. To address these issues, the federated edge learning (FEEL) framework has been developed [2]–[4], which implements distributed machine learning at the network edge. In particular, a server updates a global model by aggregating local models (or stochastic gradients) transmitted by devices that are computed using local datasets. The updating of the global model using local models and the reverse are iterated till they converge. Besides preserving data privacy by avoiding data uploading, FEEL leverages distributed computation resources as well as allows rapid access to the real-time data generated by edge devices. One focus in the research area is communication-efficient FEEL where wireless techniques are designed to accelerate learning by reducing communication overhead and latency. However, the topic of energy-efficient communication for FEEL so far has not been explored. This is an important topic as training and transmission of large-scale models are energy consuming, while most edge devices especially sensors have limited battery lives. This topic is investigated in the current work where novel radio-resource-management (RRM) strategies for joint bandwidth allocation and user scheduling are proposed for minimizing the total device energy-consumption under a constraint on the learning speed. Figure 1.

A framework for FEEL system.