Distilled one-shot federated learning for heterogeneous residential load forecasting

Chengcheng Guan; Zongchao Xie; Zhinan Ding; Xinlin Xie

doi:10.1117/12.3031354

19 July 2024 Distilled one-shot federated learning for heterogeneous residential load forecasting

Chengcheng Guan, Zongchao Xie, Zhinan Ding, Xinlin Xie

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131817U (2024) https://doi.org/10.1117/12.3031354
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Load prediction is the foundation of power system operation and analysis. It also plays a decisive role in the safety, stability, and economic operation of the smart grid. Traditional load forecasting methods are too difficult to meet the needs of the modern smart grid. As technology evolves, federated learning (FL) offers a new solution for load forecasting. However, in the training of global model, FL multiple iterations are needed to converge, which largely depends on the repeated transmission of model parameter updates between client and server. When the global model structure is complicated or the local data heterogeneity of each client is serious, more iterations are needed to converge the model. Therefore, we designed a load prediction method based on distillated one-shot federated learning to solve these problems. The proposed method distills the training data through a dataset distillation algorithm and uses the distilled data instead of gradients for transmission, greatly reducing communication costs. In addition, we propose a double-weight self-checking mechanism to avoid each client updating the global model of the server on average and improve the accuracy of load forecasting. The results of the British example show that the prediction performance and privacy of distillated one-shot federated learning are better than those of FedAvg and FedDM. Particularly, our proposed method requires only 0.28 MB of communication consumption during the training process, which is much lower than the above two algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chengcheng Guan, Zongchao Xie, Zhinan Ding, and Xinlin Xie "Distilled one-shot federated learning for heterogeneous residential load forecasting", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131817U (19 July 2024); https://doi.org/10.1117/12.3031354

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KEYWORDS

Data modeling

Machine learning

Data communications

Performance modeling

Power grids

Telecommunications

Data transmission

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