# Fed-NILM: A Federated Learning-based Non-Intrusive Load Monitoring Method for Privacy-Protection

arXiv: https://arxiv.org/abs/2105.11085 · HTML: https://mlsanity.com/paper/2105.11085/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [Subtask Gated Networks for Non-Intrusive Load Monitoring](https://mlsanity.com/paper/1811.06692/) (2018)
- [Convolutional Sequence to Sequence Non-intrusive Load Monitoring](https://mlsanity.com/paper/1806.02078/) (2018)
- [Wavenilm: A causal neural network for power disaggregation from the complex power signal](https://mlsanity.com/paper/1902.08736/) (2019)
- [On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction](https://mlsanity.com/paper/1802.02139/) (2018)
- [Non-Intrusive Load Monitoring with Fully Convolutional Networks](https://mlsanity.com/paper/1812.03915/) (2018)

## Built on by (most cited first, 4 of 4)

- [A Review of Federated Learning in Energy Systems](https://mlsanity.com/paper/2208.10941/) (2022)
- [Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method](https://mlsanity.com/paper/2410.15379/) (2024)
- [DP2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring](https://mlsanity.com/paper/2207.00041/) (2022)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)

## Abstract (arXiv)

Non-intrusive load monitoring (NILM) is essential for understanding customer's power consumption patterns and may find wide applications like carbon emission reduction and energy conservation. The training of NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners during the NILM model training. To prevent such potential risks, a novel NILM method named Fed-NILM which is based on Federated Learning (FL) is proposed in this paper. In Fed-NILM, local model parameters instead of local load data are shared among multiple data owners. The global model is obtained by weighted averaging the parameters. Experiments based on two measured load datasets are conducted to explore the generalization ability of Fed-NILM. Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained NILM is conducted. The experimental results show that Fed-NILM has superior performance in scalability and convergence. Fed-NILM outperforms locally-trained NILMs operated by local data owners and approximates the centrally-trained NILM which is trained on the entire load dataset without privacy protection. The proposed Fed-NILM significantly improves the co-modeling capabilities of local data owners while protecting power consumers' privacy.