【Author】
Huang, Gang; Wu, Chao; Hu, Yifan; Guo, Chuangxin
【Source】CHINESE PHYSICS B
【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.
【Keywords】smart grid; physical system; distributed learning; artificial intelligence
【摘要】物理系统的数字化、信息化、智能化需要大数据分析的有力支撑。然而，由于对数据安全和隐私的限制以及对大数据采集、传输和存储成本的担忧，在现实世界的电力系统中很难进行数据聚合，直接阻碍了智能电网分析的有效实施。联邦学习是谷歌提出的一种先进的分布式学习方法，似乎是解决上述问题的一个有希望的解决方案。然而，它依赖于一个服务器节点来完成模型聚合，并且该框架仅限于数据独立同分布的场景。因此，我们在这里提出了一个基于区块链的无服务器分布式学习平台来解决上述两个问题。在所提出的平台中，机器学习的任务是根据智能合约执行的，并且通过知识蒸馏机制聚合加密模型。通过这种提出的方法，不再需要服务器节点，学习能力也不再局限于独立同分布的场景。在公共电网数据集上的实验将验证所提出方法的有效性。
【关键词】智能电网;物理系统；分布式学习；人工智能
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