Work

Energy-Efficient Federated Learning for AIoT Using Clustering Methods

Year: 2025

Type: article

Abstract: While substantial research has been devoted to optimizing model performance, convergence rates, and communication efficiency, the energy implications of federated learning (FL) within Artificial Intelligence of Things (AIoT) scenarios are often overlooked in the existing literature. This study examines the energy consumed during the FL process, focusing on three main energy-intensive processes: pre-processing, communication, and local learning, all contributing to the overall energy footprint. We rely on the observation that device/client selection is crucial for speeding up the convergence of model training in a distributed AIoT setting and propose two clustering-informed methods. These clustering solutions are designed to group AIoT devices with similar label distributions, resulting in clusters composed of nearly heterogeneous devices. Hence, our methods alleviate the heterogeneity often encountered in real-world distributed learning applications. Throughout extensive numerical experimentation, we demonstrate that our clustering strategies typically achieve high convergence rates while maintaining low energy consumption when compared to other recent approaches available in the literature.

Source: IEEE Internet of Things Journal

DOI: 10.1109/jiot.2025.3579583

Authors: Roberto Pereira, Fernanda Famá, Charalampos Kalalas, Paolo Dini

Institution: Centre Tecnologic de Telecomunicacions de Catalunya

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: green

Funders: H2020 Marie Skłodowska-Curie Actions, CHIST-ERA