Communication-Efficient Federated Learning for Power Load Forecasting in Electric IoTs

Communication-Efficient Federated Learning for Power Load Forecasting in Electric IoTs

With the construction of the modern power system, power load forecasting is significant to keep the electric Internet of Things in operation. However, it usually needs to collect massive power load data on the server and may face the problem of privacy leakage of raw data. Federated learning can enh...

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Bibliographic Details
Main Authors: Zhengxiong Mao, Hui Li, Zuyuan Huang, Chuanxu Yang, Yanan Li, Zihao Zhou
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Power load forecasting
federated learning
quantization
lazy upload
error compensation
Online Access:https://ieeexplore.ieee.org/document/10081317/

Internet

https://ieeexplore.ieee.org/document/10081317/

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