@inproceedings{3e8ffd1b815248c3b404b6ba344c0fd9,
title = "Cooperative Training over Networks via Consensus-based Algorithms",
abstract = "In this paper, the training problem for a group of neural networks with private datasets is considered. Approximated gradients are employed to replace the true gradients in the proposed algorithms, due to the presence of gradient noises in the training problems. Consensus tools are used to achieve identical weights of the distributed neural networks trained using local dataset only. The convergence of the proposed algorithms is established by exploring the error dynamics of the connected agents, through which upper bounds for the learning rates are derived. Performances are analysed for the proposed algorithms with and without gradient noises. Simulation examples are provided to validate the effectiveness of the proposed algorithms.",
keywords = "Consensus, convergence analysis, distributed training, multi-agent systems, neural networks, optimisation",
author = "Zhongguo Li and Bo Liu and Zhen Dong and Zhengtao Ding",
note = "Publisher Copyright: {\textcopyright} 2021 Technical Committee on Control Theory, Chinese Association of Automation.; 40th Chinese Control Conference, CCC 2021 ; Conference date: 26-07-2021 Through 28-07-2021",
year = "2021",
month = jul,
day = "26",
doi = "10.23919/CCC52363.2021.9549958",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "5309--5314",
editor = "Chen Peng and Jian Sun",
booktitle = "Proceedings of the 40th Chinese Control Conference, CCC 2021",
address = "美国",
}