# Federated Learning based Energy Demand Prediction with Clustered Aggregation

arXiv: https://arxiv.org/abs/2210.15850 · HTML: https://mlsanity.com/paper/2210.15850/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)

## Built on by (most cited first, 9 of 9)

- [Federated Learning Priorities Under the European Union Artificial Intelligence Act](https://mlsanity.com/paper/2402.05968/) (2024)
- [Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives](https://mlsanity.com/paper/2304.08602/) (2023)
- [Energy Prediction using Federated Learning](https://mlsanity.com/paper/2301.09165/) (2023)
- [Secure short-term load forecasting for smart grids with transformer-based federated learning](https://mlsanity.com/paper/2310.17477/) (2023)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)
- [Overpredictive Signal Analytics in Federated Learning: Algorithms and Analysis](https://mlsanity.com/paper/2410.01399/) (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)
- [FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting](https://mlsanity.com/paper/2504.20282/) (2025)
- [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)

To reduce negative environmental impacts, power stations and energy grids need to optimize the resources required for power production. Thus, predicting the energy consumption of clients is becoming an important part of every energy management system. Energy usage information collected by the clients' smart homes can be used to train a deep neural network to predict the future energy demand. Collecting data from a large number of distributed clients for centralized model training is expensive in terms of communication resources. To take advantage of distributed data in edge systems, centralized training can be replaced by federated learning where each client only needs to upload model updates produced by training on its local data. These model updates are aggregated into a single global model by the server. But since different clients can have different attributes, model updates can have diverse weights and as a result, it can take a long time for the aggregated global model to converge. To speed up the convergence process, we can apply clustering to group clients based on their properties and aggregate model updates from the same cluster together to produce a cluster specific global model. In this paper, we propose a recurrent neural network based energy demand predictor, trained with federated learning on clustered clients to take advantage of distributed data and speed up the convergence process.