Exploring Lightweight Federated Learning for Distributed Load Forecasting (2404.03320v1)

Abstract: Federated Learning (FL) is a distributed learning scheme that enables deep learning to be applied to sensitive data streams and applications in a privacy-preserving manner. This paper focuses on the use of FL for analyzing smart energy meter data with the aim to achieve comparable accuracy to state-of-the-art methods for load forecasting while ensuring the privacy of individual meter data. We show that with a lightweight fully connected deep neural network, we are able to achieve forecasting accuracy comparable to existing schemes, both at each meter source and at the aggregator, by utilising the FL framework. The use of lightweight models further reduces the energy and resource consumption caused by complex deep-learning models, making this approach ideally suited for deployment across resource-constrained smart meter systems. With our proposed lightweight model, we are able to achieve an overall average load forecasting RMSE of 0.17, with the model having a negligible energy overhead of 50 mWh when performing training and inference on an Arduino Uno platform.

• The paper demonstrates a lightweight federated learning method that achieves competitive load forecasting accuracy with an RMSE of 0.17 while minimizing computational overhead.
• It employs a privacy-preserving, cluster-based approach to train decentralized deep neural networks on smart meter data for short-term predictions.
• The research validates the model’s scalability and practical deployment on resource-constrained devices, such as Arduino Uno, highlighting its efficiency and robustness.

Exploring Lightweight Federated Learning for Distributed Load Forecasting

Introduction

The exploration of Federated Learning (FL) for distributed load forecasting presents a compelling pivot from centralized approaches, particularly in the field of smart meter data analysis for electricity demand forecasting. The research underscores the strategic shift towards leveraging FL within the constraints of privacy-preserving paradigms and minimizing resources for computation-intensive tasks. By deploying a lean yet effective fully connected deep neural network (DNN) model, this paper bridges the gap between theoretical FL applicability and practical efficiency in real-world smart meter infrastructures.

Federated Learning Framework

The cornerstone of this research lies in its novel approach to applying federated learning for short-term load forecasting. FL's model allows for collaborative, localized training of individual models without sharing sensitive data, addressing significant privacy concerns inherent in centralized systems. The paper elucidates the methodical adaptation of the FL algorithm from previous foundational works, ensuring detailed technical coherence and compatibility. The federated setup enables dynamic updating of both local and global models, reflecting real-time consumption changes while optimizing for non-independent and identically distributed (i.i.d.) conditions.

Methodological Detailing

The proposed methodology elucidates a meticulous process starting from the utilization of the London household energy consumption dataset to the formation of training and test sets. Intriguingly, the research employs a clustering approach to group households based on energy consumption characteristics, further enhancing the model’s predictive accuracy by acknowledging spatial and consumption heterogeneity. Such clustering augments FL's effectiveness, providing a nuanced understanding of localized energy patterns.

Lightweight Model Architectural Insights

A pivotal contribution of this paper is the design and implementation of a lightweight DNN within an FL framework tailored for load forecasting. The model's architecture is strategically simplified to ensure feasibility across resource-constrained devices like smart meters. This aspect of the research strikes a balance between model complexity and operational exigency, making it immensely relevant for scalable deployments.

Empirical Evaluation and Insights

This paper presents a comprehensive analytical narrative covering various facets of the FL framework's performance. The empirical analysis reveals that the lightweight model achieves remarkably similar accuracy to more complex FL schemes, with an overall average load forecasting Root Mean Square Error (RMSE) of 0.17. Notably, the model demonstrates computational efficiency, showcasing negligible energy overhead on hardware platforms akin to Arduino Uno, which aligns with the practical feasibility of deploying such models in real-world scenarios.

Comparative Analysis

The research doesn't shy away from rigorous comparative analysis, benchmarking its FL approach against existing methodologies. By evaluating the model against centralized setups and other research works, it provides a quantifiable validation of its claims. The model's performance, benchmarked through RMSE and Mean Absolute Percentage Error (MAPE), competitively positions it as a viable alternative, striking a conscientious balance between accuracy, privacy preservation, and computational frugality.

Conclusions and Forward Look

The investigation concludes on a prophetic note, positing the deployment of lightweight FL models for load forecasting as not just viable but substantially beneficial in addressing privacy, accuracy, and resource consumption concerns. The research hints at a burgeoning field of paper where decentralized learning mechanisms could significantly contribute to smart grid optimization without the encumbrance of privacy violations or resource-intensive computations. The concluding remarks open avenues for future research, particularly in enhancing model accuracy and further minimizing resource overheads, paving the way for widespread adoption in smart grids and beyond.

The thoughtful execution and presentation of this research underscore the maturing landscape of federated learning applications in energy systems, reinforcing the potential of FL to revolutionize how data-driven insights are harnessed in privacy-sensitive, resource-constrained environments.