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Title	Federated Deep Learning Technique For Power And Energy Systems Data Analysis
ID_Doc	26322
Authors	Moayyed H.; Moradzadeh A.; Mohammadi-Ivatloo B.; Ghorbani R.
Year	2022
Published	Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems
DOI	http://dx.doi.org/10.1002/9781119834052.ch19
Abstract	In recent years, there has been a tremendous increase in the application of information and communication technologies (ICTs) in critical infrastructures, such as energy grids and communication infrastructures. Smart grids, smart cities, including novel power and energy systems, etc. are current ICT developments that are becoming more complex by the day. It is foreseeable that the future of power and energy systems will be different from today's situation due to much more decentralization, enhanced communication, monitoring capabilities, big data, and cyber-security threat. Although various machine learning and deep learning models have been applied to the related tasks, there is still a lack of powerful techniques to equip power and energy systems for a more complex future. Recent advances in artificial intelligence techniques have led Google to develop a new technique, federated deep learning. The use of this new technique would have many advantages, such as the ability to present a global server, cover difficulties in data collection, privacy preservation, high-performance computing, and practical data adaptation. This technique has been used in various fields recently and has attracted much attention and interest. However, it has rarely been applied in power and energy systems. In this chapter, we propose to introduce the federated algorithm in detail, present its most interesting features, and summarize recent applications of this technique in power and energy systems. © 2023 The Institute of Electrical and Electronics Engineers, Inc.
Author Keywords	Data analysis; Deep learning; Energy management; Federated learning; Power systems

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