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PFDRL: Personalized Federated Deep Reinforcement Learning for Residential Energy Management

Authors:

Fateme Nikseresht,

Viswajith Govinda Rajan,

Bradford CampbellAuthors Info & Claims

ICPP '23: Proceedings of the 52nd International Conference on Parallel Processing

Pages 402 - 411

https://doi.org/10.1145/3605573.3605641

Published: 13 September 2023 Publication History

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Abstract

The rise of the Internet of Things (IoT) has increased standby energy consumption due to the growing number of smart devices in homes. Existing approaches use real-time energy data and machine learning to identify and minimize standby energy for residential energy management but rely on cloud-based data aggregation and collaborative training due to limited edge device data. However, such an approach incurs extra cloud service costs, risks personal data leakage, and fails to capture residence diversity, resulting in suboptimal energy management performance.

In this paper, we propose a privacy-preserving and cloud-service-free residential energy management system (EMS) that utilizes personalized federated deep reinforcement learning (PFDRL) to reduce household standby energy consumption. PFDRL consists of three components: First, we develop a decentralized federated learning (DFL) framework instead of using a centralized cloud service to aggregate the model to keep both the data and the model in the local area. Second, we apply DFL with deep reinforcement learning (DRL) to share the EMS plan among local residences for collaborative training. Third, we divide the neural network in the DRL into two parts, base layers and personalization layers to enhance model convergence while maximizing EMS performance for each client in the system. We evaluate the proposed PFDRL framework on the real-world Pecan Street dataset [3], demonstrating superior performance compared to centralized settings and conventional solutions.

References

[1]

2020. Energy Information Administration. https://www.eia.gov/ (2020).

[2]

2020. Texas Electricity Rates. https://comparepower.com (2020).

[3]

2021. Dataport Database. https://dataport.pecanstreet.org.

[4]

2021. Help Net Security.https://www.helpnetsecurity.com (2021).

[5]

U. Aivodji. 2019. IOTFLA: A secured and privacy-preserving smart home architecture implementing federated learning. In SPW.

[6]

M. Al Faruque and K. Vata. 2015. Energy management-as-a-service over fog computing platform. IEEE internet of things journal (2015).

[7]

Lijuan Cao. 2003. Support vector machines experts for time series forecasting. Neurocomputing 51 (2003), 321–339.

[8]

G. Din, A. Mauthe, and A. Marnerides. 2018. Appliance-level short-term load forecasting using deep neural networks. In ICCNC.

[9]

Ali Fallah and Aryan Mokhtari. 2020. Personalized federated learning: A meta-learning approach. arXiv preprint arXiv:2002.07948 (2020).

[10]

Jiechao Gao, Mingyue Tang, Tianhao Wang, and Bradford Campbell. 2022. PFed-LDP: A Personalized Federated Local Differential Privacy Framework for IoT Sensing Data. In Sensys.

[11]

Jiechao Gao, Wenpeng Wang, Zetian Liu, Md Fazlay Rabbi Masum Billah, and Bradford Campbell. 2021. Decentralized federated learning framework for the neighborhood: a case study on residential building load forecasting. In Sensys.

[12]

Jonas Geiping and Hartmut Bauermeister. 2020. Inverting gradients-how easy is it to break privacy in federated learning?NeurIPS (2020).

[13]

K Gram-Hanssen. 2010. Standby consumption in households analyzed with a practice theory approach. Journal of Industrial Ecology (2010).

[14]

J. Haj-Yahya. 2020. Techniques for Reducing the Connected-Standby Energy Consumption of Mobile Devices. In 2020 IEEE HPCA.

[15]

Yaochen Hu and Di Niu. 2019. FDML: A collaborative machine learning framework for distributed features. In Proceedings of the 25th SIGKDD.

Digital Library

[16]

W. Kong. 2017. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE TSG (2017).

[17]

[17] Lawrence Berkeley Laboratory. 2008. https://standby.lbl.gov/docs/.

[18]

S. Lee. 2020. Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources. IEEE TII.

[19]

M. Lu and J. Lai. 2020. Review on carbon emissions of commercial buildings. Renewable and Sustainable Energy Reviews (2020).

[20]

R Lu. 2019. Demand response for home energy management using reinforcement learning and artificial neural network. TSG.

[21]

X. Luo. 2019. Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands. AEI.

[22]

Gregory P Meyer. 2021. An alternative probabilistic interpretation of the Huber loss. In Proceedings of the IEEE/CVF CVPR.

[23]

Yusuf Ozturk and Prakash Jha. 2013. A personalized home energy management system for residential demand response. In 4th ICPEEED.

[24]

P Raj, M Sudhakaran, P Philomen-D-Anand Raj, 2009. Estimation of standby power consumption for typical appliances. JESTR (2009).

[25]

M. Soliman and T. Abiodun. 2013. Smart home: Integrating internet of things with web services and cloud computing. In IEEE ICCCTS.

[26]

Idil Sülo and Theodore Brown. 2019. Energy efficient smart buildings: LSTM neural networks for time series prediction. In Deep-ML.

[27]

Afaf Taïk and Soumaya Cherkaoui. 2020. Electrical load forecasting using edge computing and federated learning. In ICC. IEEE.

[28]

Lin Wang. 2015. Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. ESWA.

[29]

S. Wang, T. Tuor, and T. Salonidis. 2019. Adaptive Federated Learning in Resource Constrained Edge Computing Systems. IEEE JSAC (2019).

[30]

W Wang, J Su, Za Hicks, and Bradford Campbell. 2020. The Standby Energy of Smart Devices: Problems, Progress, & Potential. In IoTDI.

[31]

Q Wu and X Chen. 2020. FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring. TMC (2020).

[32]

W Xu. 2019. A hybrid modeling method for time series forecasting based on a linear regression model and deep learning. AI (2019).

[33]

Xu Xu and Youwei Jia. 2020. A multi-agent reinforcement learning-based data-driven method for home energy management. TSG (2020).

[34]

L. Yang, X. Chen, J. Zhang, and H. Poor. 2014. Optimal privacy-preserving energy management for smart meters. In IEEE INFOCOM.

Cited By

Xie TLiu FGao JWang Y(2025)Integrating Consortium Blockchain and Attribute-Based Searchable Encryption for Automotive Threat Intelligence Sharing ModelScience of Cyber Security10.1007/978-981-96-2417-1_19(350-368)Online publication date: 4-Mar-2025
https://doi.org/10.1007/978-981-96-2417-1_19
Yao DZhu ZLiu TXu ZJin H(2024)Rethinking Personalized Federated Learning from Knowledge PerspectiveProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673112(991-1000)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673112
Wang JGao J(2024)Federated Learning with Knowledge Distillation to Mitigate Catastrophic Forgetting and Data Heterogeneity in IoV Systems2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825540(2914-2923)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825540
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Published In

cover image ACM Other conferences

ICPP '23: Proceedings of the 52nd International Conference on Parallel Processing

August 2023

858 pages

ISBN:9798400708435

DOI:10.1145/3605573

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2023

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Research-article
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Refereed limited

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NobleReach
Commonwealth Cyber Initiative
NSF (National Science Foundation)

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ICPP 2023

ICPP 2023: 52nd International Conference on Parallel Processing

August 7 - 10, 2023

UT, Salt Lake City, USA

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Overall Acceptance Rate 91 of 313 submissions, 29%

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Cited By

Xie TLiu FGao JWang Y(2025)Integrating Consortium Blockchain and Attribute-Based Searchable Encryption for Automotive Threat Intelligence Sharing ModelScience of Cyber Security10.1007/978-981-96-2417-1_19(350-368)Online publication date: 4-Mar-2025
https://doi.org/10.1007/978-981-96-2417-1_19
Yao DZhu ZLiu TXu ZJin H(2024)Rethinking Personalized Federated Learning from Knowledge PerspectiveProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673112(991-1000)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673112
Wang JGao J(2024)Federated Learning with Knowledge Distillation to Mitigate Catastrophic Forgetting and Data Heterogeneity in IoV Systems2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825540(2914-2923)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825540
Gao JLi Y(2024)FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10821859(6384-6391)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10821859

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