Towards Energy Efficient Distributed Federated Learning for 6G Networks

TL;DR

This paper introduces a distributed federated learning framework for 6G networks that enhances classification accuracy and reduces energy consumption for distant devices, addressing privacy, connectivity, and energy efficiency issues.

Contribution

It proposes a novel distributed federated learning framework compatible with mobile edge computing, improving performance and energy efficiency over traditional federated learning.

Findings

Increases classification performance by 7.4%

Reduces energy consumption for remote devices

Addresses privacy and connectivity challenges in 6G networks

Abstract

The provision of communication services via portable and mobile devices, such as aerial base stations, is a crucial concept to be realized in 5G/6G networks. Conventionally, IoT/edge devices need to transmit the data directly to the base station for training the model using machine learning techniques. The data transmission introduces privacy issues that might lead to security concerns and monetary losses. Recently, Federated learning was proposed to partially solve privacy issues via model-sharing with base station. However, the centralized nature of federated learning only allow the devices within the vicinity of base stations to share the trained models. Furthermore, the long-range communication compels the devices to increase transmission power, which raises the energy efficiency concerns. In this work, we propose distributed federated learning (DBFL) framework that overcomes the connectivity and energy efficiency issues for distant devices. The DBFL framework is compatible with mobile edge computing architecture that connects the devices in a distributed manner using clustering protocols. Experimental results show that the framework increases the classification performance by 7.4\% in comparison to conventional federated learning while reducing the energy consumption.