# FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers

arXiv: https://arxiv.org/abs/2203.00219 · HTML: https://mlsanity.com/paper/2203.00219/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Electrical Load Forecasting Using Edge Computing and Federated Learning](https://mlsanity.com/paper/2201.11248/) (2022)
- [Time Series Forecasting Using LSTM Networks: A Symbolic Approach](https://mlsanity.com/paper/2003.05672/) (2020)
- [Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting](https://mlsanity.com/paper/2111.07462/) (2021)
- [False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey](https://mlsanity.com/paper/2111.14251/) (2021)

## Built on by (most cited first, 14 of 14)

- [PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning](https://mlsanity.com/paper/2502.00354/) (2025)
- [A Secure Federated Learning Framework for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2209.14547/) (2022)
- [Federated Learning with Domain Shift Eraser](https://mlsanity.com/paper/2503.13063/) (2025)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning](https://mlsanity.com/paper/2410.07738/) (2024)
- [CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment](https://mlsanity.com/paper/2307.12149/) (2023)
- [FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination](https://mlsanity.com/paper/2303.16956/) (2023)
- [FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination](https://mlsanity.com/paper/2304.03640/) (2023)
- [Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity](https://mlsanity.com/paper/2405.20589/) (2024)
- [Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach](https://mlsanity.com/paper/2407.15879/) (2024)
- [FedSAF: A Federated Learning Framework for Enhanced Gastric Cancer Detection and Privacy Preservation](https://mlsanity.com/paper/2503.15870/) (2025)
- [Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data](https://mlsanity.com/paper/2505.02540/) (2025)
- [Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization](https://mlsanity.com/paper/2509.10521/) (2025)
- [Personalized federated prototype learning in mixed heterogeneous data scenarios](https://mlsanity.com/paper/2510.03726/) (2025)

## Abstract (arXiv)

As Smart Meters are collecting and transmitting household energy consumption data to Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine-grained consumer data while ensuring data privacy. In this manuscript, we tackle this challenge for energy load consumption forecasting in regards to REPs which is essential to energy demand management, load switching and infrastructure development. Specifically, we note that existing energy load forecasting is centralized, which are not scalable and most importantly, vulnerable to data privacy threats. Besides, REPs are individual market participants and liable to ensure the privacy of their own customers. To address this issue, we propose a novel horizontal privacy-preserving federated learning framework for REPs energy load forecasting, namely FedREP. We consider a federated learning system consisting of a control centre and multiple retailers by enabling multiple REPs to build a common, robust machine learning model without sharing data, thus addressing critical issues such as data privacy, data security and scalability. For forecasting, we use a state-of-the-art Long Short-Term Memory (LSTM) neural network due to its ability to learn long term sequences of observations and promises of higher accuracy with time-series data while solving the vanishing gradient problem. Finally, we conduct extensive data-driven experiments using a real energy consumption dataset. Experimental results demonstrate that our proposed federated learning framework can achieve sufficient performance in terms of MSE ranging between 0.3 to 0.4 and is relatively similar to that of a centralized approach while preserving privacy and improving scalability.