A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction

no code implementations • 8 Jan 2022 • Haizhou Liu, Xuan Zhang, Xinwei Shen, Hongbin Sun

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.

Fairness Privacy Preserving +1

A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning

no code implementations • 22 Mar 2021 • Haizhou Liu, Xuan Zhang, Xinwei Shen, Hongbin Sun

For example, power consumption data are stored in numerous transformer stations across cities; mobility data of the population, which are important indicators of power consumption, are held by mobile companies.

Privacy Preserving Vertical Federated Learning