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

TL;DR

This paper introduces innovative transmission and resource allocation strategies for energy-efficient massive MIMO systems supporting federated learning, focusing on synchronous, asynchronous, and session-based designs to optimize energy use and performance.

Contribution

It presents novel FL-specific transmission schemes and algorithms for massive MIMO that improve energy efficiency and resource utilization over existing methods.

Findings

Session-based design improves energy efficiency and transmission speed.

Algorithms effectively minimize energy consumption while meeting time constraints.

Asynchronous design offers flexible energy savings for users.

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, leading to higher rates, lower transmission times, and hence, a 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 one FL communication round.