Communication-Efficient Federated Learning for Power Load Forecasting in Electric IoTs-Reference-Cited by-同舟云学术

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

Author:

Mao Zhengxiong¹,Li Hui¹,Huang Zuyuan¹,Yang Chuanxu¹,Li Yanan²,Zhou Zihao³^ORCID

Affiliation:

1. Network Information Center of Yunnan State Grid Company, Kunming, China

2. School of Software, Henan Polytechnic University, Jiaozuo, China

3. School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China

Funder

Yunnan Power Grid Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference32 articles.

3. Deep gradient compression: Reducing the communication bandwidth for distributed training;lin;arXiv 1712 01887,2017

4. signSGD: Compressed optimisation for non-convex problems;bernstein;Proc Int Conf Mach Learn,2018

5. A generic framework for privacy preserving deep learning;ryffel;arXiv 1811 04017,2018

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