Energy-Efficient Radio Resource Allocation for Federated Edge Learning

Abstract

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.

Cited by in corpus (5)

• Multi-Armed Bandit Based Client Scheduling for Federated Learning
• Delay Minimization for Federated Learning Over Wireless Communication Networks
• Wireless Federated Learning with Local Differential Privacy
• Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learning
• Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design