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A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction by Liu, Haizhou; Zhang, Xuan; Shen, Xinwei; Sun, Hongbin is a scholarly article available to read on EtoBox.

What is A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction about?

In a modern power system, real-time data on power generation/consumption and its relevant features are stored in various distributed parties, including household meters, transformer stations and external organizations. To fully exploit the underlying patterns of these distributed data for accurate power prediction, federated learning is needed as a collaborative but privacy-preserving training scheme. However, current federated learning frameworks are polarized towards addressing either the horizontal or vertical separation of data, and tend to overlook the case where both are present. Furthermore, in mainstream horizontal federated learning frameworks, only artificial neural networks are employed to learn the data patterns, which are considered less accurate and interpretable compared to tree-based models on tabular datasets. To this end, we propose a hybrid federated learning framework based on XGBoost, for distributed power prediction from real-time external features. In addition to introducing boosted trees to improve accuracy and interpretability, we combine horizontal and vertical federated learning, to address the scenario where features are scattered in local heterogeneous

Author

Liu, Haizhou; Zhang, Xuan; Shen, Xinwei; Sun, Hongbin

Published

2022

Language

EN