Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations

Abstract

This work proposes novel synchronous, asynchronous, and session-based designs for energy-efficient massive multiple-input multiple-output networks to support federated learning (FL). The synchronous design relies on strict synchronization among users when executing each FL communication round, while the asynchronous design allows more flexibility for users to save energy by using lower computing frequencies. The session-based design splits the downlink and uplink phases in each FL communication round into separate sessions. In this design, we assign users such that one of the participating users in each session finishes its transmission and does not join the next session. As such, more power and degrees of freedom will be allocated to unfinished users, resulting in higher rates, lower transmission times, and hence, higher energy efficiency. In all three designs, we use zero-forcing processing for both uplink and downlink, and develop algorithms that optimize user assignment, time allocation, power, and computing frequencies to minimize the energy consumption at the base station and users, while guaranteeing a predefined maximum execution time of each FL communication round.

A Novel Approach for Scalable and Sustainable 6G Networks

“…The vision for distributed network management and orchestration proposed in this paper is also aligned with and mapped to existing standardization architectures (including the 3GPP, the ETSI and the ITU) to enable hierarchical and distributed management structures. The benefits of distributed management and orchestration, especially with regard to energy savings through FL, have been investigated in various studies [27], [28], [29], [30], [31]. While previous studies have recognised the importance of distributed approaches for energy savings, we provide a unique quantitative analysis of the energy efficiency improvements achieved by FL with an experimental setup using Virtual Reality (VR) video streaming application.…”

Software-Defined GPU-CPU Empowered Efficient Wireless Federated Learning With Embedding Communication Coding for Beyond 5G

“…Researchers have tried to optimize WFL from different aspects. Under the condition of limited wireless network resources [31] and client energy resources [32]- [33] participating in WFL local training, Zhou proposed a bandwidth allocation algorithm with low energy consumption [34], which enables clients to engage in learning more sustainably. Xu proposed to intelligently select clients participating in WFL local learning based on energy consumption from the longterm perspective of learning as a whole [35], not limited to learning rounds.…”

Effective 3C Resource Utilization and Fair Allocation Strategy for Multi-Task Federated Learning

“…To enable synchronous federated learning over wireless networks, Vu et al [13] adopt multiple-input multiple-output (MIMO) and ensure stable operation during synchronous communication periods for each federated learning task. They minimize energy consumption by considering user requirements, time allocation, transmit power, and computing frequency, and use Fritz John and Karush-Kuhn-Tucker solutions to achieve stable convergence.…”