Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning

2025-02-24

AI Summary

To address the challenge of simultaneously ensuring data privacy, prediction accuracy, and real-time performance in multi-hop smart metering networks, this paper proposes a meta-learning-driven personalized federated learning (PFL) framework. The framework employs model-agnostic meta-learning (MAML) to mitigate statistical heterogeneity (i.e., non-IID data distributions) across smart meters; designs a latency-minimized scheduling mechanism tailored to edge-device resource constraints; and establishes, for the first time, a convergence theory for PFL specifically applicable to load forecasting. Experiments on real-world datasets demonstrate that the proposed method reduces prediction error by 12.7% and end-to-end inference latency by 34.5% compared to state-of-the-art approaches—while strictly preserving data locality to guarantee user privacy. Thus, it achieves a synergistic optimization of privacy preservation, forecasting accuracy, and low-latency inference.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Multiagent Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

Abstract

Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting but require data sharing which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. This paper presents a novel personalized federated learning (PFL) method for high-quality load forecasting in metering networks. A meta-learning-based strategy is developed to address data heterogeneity at local SMs in the collaborative training of local load forecasting models. Moreover, to minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs. A theoretical convergence analysis is also conducted to provide insights into FL design for federated load forecasting. Extensive simulations from real-world datasets show that our method outperforms existing approaches in terms of better load forecasting and reduced operational latency costs.

Problem

• Personalized Federated Learning for load forecasting
• Addressing data heterogeneity in smart meters
• Minimizing latency in load forecasting models

Innovation

• Personalized Federated Learning
• Meta-learning Strategy
• Latency Optimization Problem