  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
parties and samples are scattered in various local districts. Moreover, we
design a dynamic task allocation scheme such that each party gets a fair share
of information, and the computing power of each party can be fully leveraged to
boost training efficiency. A follow-up case study is presented to justify the
necessity of adopting the proposed framework. The advantages of the proposed
framework in fairness, efficiency and accuracy performance are also confirmed.
