# Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning

arXiv: https://arxiv.org/abs/2502.17226 · HTML: https://mlsanity.com/paper/2502.17226/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Federated Learning for Short-term Residential Load Forecasting](https://mlsanity.com/paper/2105.13325/) (2021)
- [Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting](https://mlsanity.com/paper/2111.07462/) (2021)
- [ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting](https://mlsanity.com/paper/2112.02663/) (2021)
- [Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach](https://mlsanity.com/paper/2411.10619/) (2024)
- [Automated Deep Learning for Load Forecasting](https://mlsanity.com/paper/2405.08842/) (2024)
- [WIP: Federated Learning for Routing in Swarm Based Distributed Multi-Hop Networks](https://mlsanity.com/paper/2303.08871/) (2023)

## Built on by (most cited first, 7 of 7)

- [Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach](https://mlsanity.com/paper/2507.08217/) (2025)
- [Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise](https://mlsanity.com/paper/2507.12492/) (2025)
- [Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence](https://mlsanity.com/paper/2504.17703/) (2025)
- [Towards Personalized Quantum Federated Learning for Anomaly Detection](https://mlsanity.com/paper/2511.07471/) (2025)
- [SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization](https://mlsanity.com/paper/2508.12477/) (2025)
- [Probabilistic Federated Learning on Uncertain and Heterogeneous Data with Model Personalization](https://mlsanity.com/paper/2603.18083/) (2026)
- [Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems](https://mlsanity.com/paper/2609.19695/) (2026)

## Abstract (arXiv)

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 regarding better load forecasting and reduced operational latency costs.