18th International Conference on Innovative Computing, Information and Control (ICICIC2024)
Onsite (Qinhuangdao, China) and Online, September 10-13, 2024

PS-24: ICICIC2024-139
Research on the Application of Federated Learning in Smart Grid1,2

3

Xiaorong Feng *, Ying Gao , Yixi Peng and Laiping Zhang

1

1

2

China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou 510610, P. R. China
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510610, P. R. China
3
School of Software Engineering, Sun Yat-Sen University, Guangzhou 510610, P. R. China
fxrsmile@163.com; gaoying@scut.edu.cn; pengyxyx@126.com*Corresponding author: fengxr@ceprei.com

Introduction
This article combines the data characteristics and needs of smart grid to study the applicability of federated learning. Fromthe perspective of holism, it surveys and sorts the current research status of federated learning frameworks in power
measurement systems, grid data sharing, wind power load prediction, privacy protection and communication energy analysis,
abnormal attack monitoring, etc. It analyzes its innovative applications in data privacy protection, distributed energymanagement system optimization, grid security monitoring and fault prediction, and evaluates its implementation effect andquality. Finally, it looks forward to the research challenges and future research directions of federated learning applications insmart grids from aspects such as data privacy protection and security, communication efficiency and computing resource
optimization, model generalization and robustness improvement, differential privacy, homomorphic encryption.

Research Questions
1)

2)
3)

Analysis of applicability of federated
learning in smart grid: including datacharacteristics and requirements ofsmart grid, federated learning’sapplicability;
Application scenarios of smart gridfederated learning;Technical analysis of federated
learning in smart grids: including
comparative analysis of data privacy
protection and security based onfederated learning, communication
efficiency and computation resource
optimization, effect evaluation andcomparative analysis.

FiguresFigure 1. A privacy-preserving computing platform based onfederated learning

Conclusion
Through in-depth research on the combination of smart grid and federated learning, this article has elaborated on theenormous potential and value of both in the energy field. Federated learning can achieve model optimization while protecting theprivacy of smart grid data, and different energy nodes can jointly train and optimize energy management models in areas suchas distributed energy management, grid security monitoring, and user-side demand response.
There are technical challenges in practical applications of federated learning for smart grids in terms of model training andoptimization, communication efficiency, and computational resource optimization under the premise of data privacy. With
continuous technological progress and in-depth application expansion, federated learning for smart grids will demonstrate its
unique advantages and value in more fields and promote innovative development of the energy system.