Energy-Efficient Federated Learning for AIoT using Clustering Methods (2505.09704v1)

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.

Energy-Efficient Federated Learning for AIoT Using Clustering Methods

The research conducted by Pereira et al. explores a critical aspect of Federated Learning (FL) in the context of Artificial Intelligence of Things (AIoT): energy efficiency. While much of the existing literature has focused on optimizing factors such as model performance, convergence rates, and communication efficiency, this paper specifically addresses the overlooked dimension of energy consumption. The paper identifies the energy-intensive components in the FL process, which include pre-processing, communication, and local learning. It proposes innovative clustering strategies aimed at achieving high convergence rates with reduced energy expenditure, distinguishing it from traditional methods biased towards minimizing communication bounds without considering the resultant pre-processing and energy costs associated with client selection mechanisms.

The primary advancement presented is the introduction of two clustering-informed methods for client selection. These methods focus on grouping AIoT devices based on their label distributions, thus addressing the heterogeneity that commonly arises in distributed learning scenarios. The clustering solutions leverage the formation of homogeneous clusters of devices, resulting in enhanced convergence speeds—key for energy savings—compared to heterogeneous setups. The methods proposed are termed as SimClust and RepClust. SimClust aims to form clusters where clients have similar label distributions, thus facilitating client selections from clusters to maintain diversity and broaden reach over the data space. RepClust, contrarily, seeks to maximize diversity within each group while ensuring the collective dataset is representative of the larger dataset.

One notable facet of the research is the empirical demonstration of how these clustering methods can lead to substantial energy savings during the learning process. This is primarily due to the more efficient use of information inherent in diverse client selections, which accelerates the convergence rate of FL systems. The research findings are supported by extensive experiments conducted over diverse datasets, including F-MNIST, CIFAR-10, and CIFAR-100. The paper evidences that the clustering strategies can achieve favorable accuracy levels with less energy, as compared to other approaches such as random client selection or adaptive methods like FedCor.

Another major contribution is the breakdown of energy consumption into pre-processing at the server, local training, and communication. The paper goes beyond traditional evaluation metrics by systematically measuring the energy cost associated with each stage using real hardware energy measurements and code instrumentation. This nuanced approach highlights the substantial energy component attributed to local learning on devices, which, when optimized, offers potential for significant energy savings across the distributed learning lifecycle.

The implications of these findings are manifold. Practically, these clustering methods can lead to the design of more energy-efficient AIoT systems, crucial for deployment where resources are constrained, such as in remote sensing or smart city applications. Theoretically, this work contributes to understanding how clustering in FL can serve multiple objectives beyond node participation fairness or network optimization, expanding its utility to sustainable computing considerations. Future research can further explore these methods in larger-scale environments and extend their applicability by integrating additional factors such as privacy preservation techniques.

In conclusion, this paper provides a compelling perspective on energy-efficient federated learning by showcasing how clustering methods can be strategically deployed to significantly reduce energy costs while maintaining or enhancing model accuracy. This balance of energy efficiency and model performance is pivotal for advancing AIoT applications, where the integration of intelligent systems seamlessly with constrained devices is of primary importance. As AI continues to infiltrate numerous domains, the findings of this research underscore the necessity for continual exploration into the multifaceted nature of energy efficiency within federated learning frameworks.