Energy-Efficient Radio Resource Allocation for Federated Edge Learning (1907.06040v1)

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.

Energy-Efficient Radio Resource Allocation for Federated Edge Learning

This paper addresses the critical issue of energy efficiency in federated edge learning (FEEL), particularly focusing on radio resource management (RRM) for bandwidth allocation and scheduling. By integrating both channel states and computation capacities, the proposed strategies aim at minimizing the energy consumption of edge devices without compromising the learning performance. The methodological advancements presented in this paper contribute to the field of edge computing by innovatively tackling the energy constraint problem in the context of distributed machine learning.

Overview of Federated Edge Learning and RRM

Federated edge learning is an emerging framework that coordinates global model training at a central server with local model updates at distributed edge devices over wireless networks. This architecture is particularly advantageous for preserving data privacy and enhancing resource efficiency by eliminating the need for raw data transmission to the cloud. Within this FEEL paradigm, managing communication resources efficiently, particularly in terms of energy consumption, is crucial given the limited battery life of edge devices.

The paper identifies a gap in existing research which largely focuses on communication efficiency—in terms of reducing latency and bandwidth use—without considering the energy implications of training and transmitting large-scale models. The authors propose novel methods for energy-efficient RRM, which consider both the wireless channel characteristics and the computational capabilities of edge devices.

Proposed Energy-Efficient RRM Strategies

Two main strategies are introduced:

1. Bandwidth Allocation: This strategy assumes a set schedule of devices and optimizes bandwidth allocation to minimize energy consumption. The derived policy contrasts traditional rate-maximization approaches, allocating more bandwidth to devices with poorer computation capacities and weaker channels, which are typically synchronized update bottlenecks.
2. Joint Scheduling and Bandwidth Allocation: Extending the first strategy, this joint solution incorporates device scheduling to decide which devices participate in FEEL. Using derived scheduling priority functions, the proposed method selects devices with optimal channels and computation capacities, while managing bandwidth to minimize energy consumption effectively.

The results from these strategies demonstrate a substantial reduction in energy consumption without sacrificing learning speed or accuracy, underlining the effectiveness of the proposed methods.

Implications and Future Work

The implications of this research are significant for both practical applications and theoretical developments in FEEL and wireless communication. Practically, deploying these energy-efficient strategies in real-world scenarios can enable sustainable and scalable edge learning environments, especially in resource-constrained settings. Theoretically, the paper sets a foundation for further explorations into RRM strategies that integrate additional factors such as asynchronous updates or more dynamic channel conditions.

Future work could potentially explore the integration of more sophisticated energy consumption models that consider the specific characteristics of local computations and the impact of various machine learning algorithms. Additionally, the exploration of hybrid solutions that combine the proposed strategies with techniques like analog aggregation could further enhance the efficiency of the FEEL systems.

Overall, this work contributes valuable insights and methodologies to the area of federated learning and edge computing, showcasing a crucial step towards sustainable and efficient machine learning deployment at the network edge.