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A Survey of Integrating Federated Learning with Smart Grids: Application Prospect, Privacy Preserving and Challenges Analysis

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Big Data and Security (ICBDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1796))

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Abstract

With the widespread promotion of smart grid, the power time series data collected by smart meters also increases rapidly. How to collect these data safely and effectively, analyze and utilize them, and provide better power supply service has become a hot topic of current research. The federated learning technology has attracted much attention from researchers in recent years and various federated learning-based applications have been utilized due to its characteristics of distributed, security, encryption, and reliability. In the development of smart grids, federated learning has been applied for data analytics, privacy preserving, energy management, and so on. This paper is aimed at exploring the feasibility of applying the federated learning framework to the area of smart grids. We conclude the analysis of power time series data, discussing the tribulations and solutions in the process of privacy preserving in the smart grid, and highlighting different challenges of federated learning with the smart grid. We present a summarization among federated learning-based methods with the smart grid for a variety of purposes, with the aim to draw a comparison among federated learning-based methods in the smart grid from different aspects.

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Acknowledgement

This work was also supported by the national natural science foundation of China (Project No.42171245), Jiangsu Province Engineering Research Center of IntelliSense Technology and System, the Scientific Research Foundation of Nanjing Institute of Technology (YKJ201922) and NARI Technology Co., Ltd.

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Authors and Affiliations

  1. School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing, 211167, Jiangsu, China

    Zhichao Tang, Yan Yan, Dong Wu, Tianhao Yang, Ruixuan Dong, Shuyang Hao, Wei Wang, Yizhi Chen & Yuan Tian

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  1. Zhichao Tang
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  2. Yan Yan
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  3. Dong Wu
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Correspondence to Yuan Tian .

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Editors and Affiliations

  1. Nanjing Institute of Technology, Nanjing, China

    Yuan Tian

  2. Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

    Tinghuai Ma

  3. Chinese Academy of Sciences, Shenzhen, China

    Qingshan Jiang

  4. Nanjing University of Information Science and Technology, Nanjing, China

    Qi Liu

  5. King Saud University, Riyadh, Saudi Arabia

    Muhammad Khurram Khan

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Tang, Z. et al. (2023). A Survey of Integrating Federated Learning with Smart Grids: Application Prospect, Privacy Preserving and Challenges Analysis. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_21

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  • DOI: https://doi.org/10.1007/978-981-99-3300-6_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3299-3

  • Online ISBN: 978-981-99-3300-6

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