Serverless distributed learning for smart grid analytics

doi:10.1088/1674-1056/abe232

中国物理B ›› 2021, Vol. 30 ›› Issue (8): 88802-088802.doi: 10.1088/1674-1056/abe232

Serverless distributed learning for smart grid analytics

Gang Huang(黄刚)^1,†, Chao Wu(吴超)², Yifan Hu(胡一帆)³, and Chuangxin Guo(郭创新)⁴

1 Zhejiang Lab, Hangzhou 311121, China;
2 School of Public Affairs, Zhejiang University, Hangzhou 310058, China;
3 Polytechnic Institute, Zhejiang University, Hangzhou 310015, China;
4 College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

收稿日期:2020-12-13 修回日期:2021-01-08 接受日期:2021-02-02 出版日期:2021-07-16 发布日期:2021-07-23
通讯作者: Gang Huang E-mail:huanggang@zju.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 52007173 and U19B2042), Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ20E070002), and Zhejiang Lab's Talent Fund for Young Professionals (Grant No. 2020KB0AA01).

Serverless distributed learning for smart grid analytics

Gang Huang(黄刚)^1,†, Chao Wu(吴超)², Yifan Hu(胡一帆)³, and Chuangxin Guo(郭创新)⁴

1 Zhejiang Lab, Hangzhou 311121, China;
2 School of Public Affairs, Zhejiang University, Hangzhou 310058, China;
3 Polytechnic Institute, Zhejiang University, Hangzhou 310015, China;
4 College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Received:2020-12-13 Revised:2021-01-08 Accepted:2021-02-02 Online:2021-07-16 Published:2021-07-23
Contact: Gang Huang E-mail:huanggang@zju.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 52007173 and U19B2042), Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ20E070002), and Zhejiang Lab's Talent Fund for Young Professionals (Grant No. 2020KB0AA01).

摘要/Abstract

摘要： The digitization, informatization, and intelligentization of physical systems require strong support from big data analysis. However, due to restrictions on data security and privacy and concerns about the cost of big data collection, transmission, and storage, it is difficult to do data aggregation in real-world power systems, which directly retards the effective implementation of smart grid analytics. Federated learning, an advanced distributed learning method proposed by Google, seems a promising solution to the above issues. Nevertheless, it relies on a server node to complete model aggregation and the framework is limited to scenarios where data are independent and identically distributed. Thus, we here propose a serverless distributed learning platform based on blockchain to solve the above two issues. In the proposed platform, the task of machine learning is performed according to smart contracts, and encrypted models are aggregated via a mechanism of knowledge distillation. Through this proposed method, a server node is no longer required and the learning ability is no longer limited to independent and identically distributed scenarios. Experiments on a public electrical grid dataset will verify the effectiveness of the proposed approach.

关键词: smart grid, physical system, distributed learning, artificial intelligence

Abstract: The digitization, informatization, and intelligentization of physical systems require strong support from big data analysis. However, due to restrictions on data security and privacy and concerns about the cost of big data collection, transmission, and storage, it is difficult to do data aggregation in real-world power systems, which directly retards the effective implementation of smart grid analytics. Federated learning, an advanced distributed learning method proposed by Google, seems a promising solution to the above issues. Nevertheless, it relies on a server node to complete model aggregation and the framework is limited to scenarios where data are independent and identically distributed. Thus, we here propose a serverless distributed learning platform based on blockchain to solve the above two issues. In the proposed platform, the task of machine learning is performed according to smart contracts, and encrypted models are aggregated via a mechanism of knowledge distillation. Through this proposed method, a server node is no longer required and the learning ability is no longer limited to independent and identically distributed scenarios. Experiments on a public electrical grid dataset will verify the effectiveness of the proposed approach.

Key words: smart grid, physical system, distributed learning, artificial intelligence

中图分类号: (Transmission grids)

88.80.hh

88.80.H- (Electric power transmission) 89.20.Ff (Computer science and technology)

Gang Huang(黄刚), Chao Wu(吴超), Yifan Hu(胡一帆), and Chuangxin Guo(郭创新). Serverless distributed learning for smart grid analytics[J]. 中国物理B, 2021, 30(8): 88802-088802.

Gang Huang(黄刚), Chao Wu(吴超), Yifan Hu(胡一帆), and Chuangxin Guo(郭创新). Serverless distributed learning for smart grid analytics[J]. Chin. Phys. B, 2021, 30(8): 88802-088802.

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Serverless distributed learning for smart grid analytics

Serverless distributed learning for smart grid analytics

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