| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
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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 |
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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.
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Received: 13 December 2020
Revised: 08 January 2021
Accepted manuscript online: 02 February 2021
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PACS:
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88.80.hh
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(Transmission grids)
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88.80.H-
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(Electric power transmission)
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89.20.Ff
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(Computer science and technology)
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| Fund: 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). |
Corresponding Authors:
Gang Huang
E-mail: huanggang@zju.edu.cn
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Cite this article:
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 2021 Chin. Phys. B 30 088802
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