# Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach

arXiv: https://arxiv.org/abs/2411.10619 · HTML: https://mlsanity.com/paper/2411.10619/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 15 Nov 2024.

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

- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Personalized Federated Learning with Moreau Envelopes](https://mlsanity.com/paper/2006.08848/) (2020)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [Error Feedback Fixes SignSGD and other Gradient Compression Schemes](https://mlsanity.com/paper/1901.09847/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization](https://mlsanity.com/paper/1905.13727/) (2019)
- [Catalyst Acceleration for First-order Convex Optimization: from Theory to Practice](https://mlsanity.com/paper/1712.05654/) (2017)
- [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)

## Built on by (most cited first, 11 of 11)

- [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)
- [Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning](https://mlsanity.com/paper/2502.17226/) (2025)
- [Towards Personalized Quantum Federated Learning for Anomaly Detection](https://mlsanity.com/paper/2511.07471/) (2025)
- [Differentially Private Federated Quantum Learning via Quantum Noise](https://mlsanity.com/paper/2508.20310/) (2025)
- [Integrating Personalized Federated Learning with Control Systems for Enhanced Performance](https://mlsanity.com/paper/2501.15728/) (2025)
- [Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow Management](https://mlsanity.com/paper/2501.16758/) (2025)
- [A Lightweight DL Model for Smart Grid Power Forecasting with Feature and Resolution Mismatch](https://mlsanity.com/paper/2510.16911/) (2025)
- [Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization](https://mlsanity.com/paper/2501.18174/) (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) are used to record household energy consumption. 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 to load prediction under non-independent and identically distributed (non-IID) metering data settings. Specifically, we introduce meta-learning, where the learning rates are manipulated using the meta-learning idea to maximize the gradient for each client in each global round. Clients with varying processing capacities, data sizes, and batch sizes can participate in global model aggregation and improve their local load forecasting via personalized learning. Simulation results show that our approach outperforms state-of-the-art ML and FL methods in terms of better load forecasting accuracy.