Efficient training: Federated learning cost analysis

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Research Pub Date : 2025-02-20 DOI:10.1016/j.bdr.2025.100510

Rafael Teixeira , Leonardo Almeida , Mário Antunes , Diogo Gomes , Rui L. Aguiar

引用次数: 0

Abstract

With the rapid development of 6G, Artificial Intelligence (AI) is expected to play a pivotal role in network management, resource optimization, and intrusion detection. However, deploying AI models in 6G networks faces several challenges, such as the lack of dedicated hardware for AI tasks and the need to protect user privacy. To address these challenges, Federated Learning (FL) emerges as a promising solution for distributed AI training without the need to move data from users' devices. This paper investigates the performance and costs of different FL approaches regarding training time, communication overhead, and energy consumption. The results show that FL can significantly accelerate the training process while reducing the data transferred across the network. However, the effectiveness of FL depends on the specific FL approach and the network conditions.

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高效训练：联邦学习成本分析

随着6G的快速发展，人工智能（AI）有望在网络管理、资源优化和入侵检测方面发挥关键作用。然而，在6G网络中部署人工智能模型面临着一些挑战，例如缺乏用于人工智能任务的专用硬件以及需要保护用户隐私。为了应对这些挑战，联邦学习（FL）成为分布式人工智能训练的一种很有前途的解决方案，无需从用户设备中移动数据。本文研究了不同的FL方法在训练时间、通信开销和能源消耗方面的性能和成本。结果表明，FL可以显著加快训练过程，同时减少跨网络传输的数据量。然而，FL的有效性取决于具体的FL方法和网络条件。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data Research Computer Science-Computer Science Applications

CiteScore

8.40

自引率

3.00%

发文量

期刊介绍： The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.