# Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecasting

arXiv: https://arxiv.org/abs/2111.07462 · HTML: https://mlsanity.com/paper/2111.07462/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 14 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning](https://mlsanity.com/paper/2103.11870/) (2021)
- [Forecasting Photovoltaic Power Production using a Deep Learning Sequence to Sequence Model with Attention](https://mlsanity.com/paper/2008.02775/) (2020)

## Built on by (most cited first, 13 of 13)

- [FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers](https://mlsanity.com/paper/2203.00219/) (2022)
- [Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning](https://mlsanity.com/paper/2502.17226/) (2025)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment](https://mlsanity.com/paper/2307.12149/) (2023)
- [Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis](https://mlsanity.com/paper/2304.13275/) (2023)
- [Exploring Lightweight Federated Learning for Distributed Load Forecasting](https://mlsanity.com/paper/2404.03320/) (2024)
- [A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction](https://mlsanity.com/paper/2201.02783/) (2022)
- [FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination](https://mlsanity.com/paper/2304.03640/) (2023)
- [FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction](https://mlsanity.com/paper/2309.10337/) (2023)
- [Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting](https://mlsanity.com/paper/2402.01546/) (2024)
- [Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach](https://mlsanity.com/paper/2407.15879/) (2024)
- [Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid](https://mlsanity.com/paper/2410.10018/) (2024)
- [Federated Learning for Global Carbon Emission Forecasting: A Hybrid Time-Series Approach with Statistical and Neural Models](https://mlsanity.com/paper/2606.22618/) (2026)

## Abstract (arXiv)

Electrical load prediction has become an integral part of power system operation. Deep learning models have found popularity for this purpose. However, to achieve a desired prediction accuracy, they require huge amounts of data for training. Sharing electricity consumption data of individual households for load prediction may compromise user privacy and can be expensive in terms of communication resources. Therefore, edge computing methods, such as federated learning, are gaining more importance for this purpose. These methods can take advantage of the data without centrally storing it. This paper evaluates the performance of federated learning for short-term forecasting of individual house loads as well as the aggregate load. It discusses the advantages and disadvantages of this method by comparing it to centralized and local learning schemes. Moreover, a new client clustering method is proposed to reduce the convergence time of federated learning. The results show that federated learning has a good performance with a minimum root mean squared error (RMSE) of 0.117kWh for individual load forecasting.