Work

Efficient training: Federated learning cost analysis

Year: 2025

Type: article

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.

Source: Big Data Research

DOI: 10.1016/j.bdr.2025.100510

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

Institution: University of Aveiro

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,840