@inproceedings{e71d90b477854cdf927eda23b7dbe3ed,
title = "FedPrune: Personalized and Communication-Efficient Federated Learning on Non-IID Data",
abstract = "Federated learning (FL) has been widely deployed in edge computing scenarios. However, FL-related technologies are still facing severe challenges while evolving rapidly. Among them, statistical heterogeneity (i.e., non-IID) seriously hinders the wide deployment of FL. In our work, we propose a new framework for communication-efficient and personalized federated learning, namely FedPrune. More specifically, under the newly proposed FL framework, each client trains a converged model locally to obtain critical parameters and substructure that guide the pruning of the network participating FL. FedPrune is able to achieve high accuracy while greatly reducing communication overhead. Moreover, each client learns a personalized model in FedPrune. Experimental results has demonstrated that FedPrune achieves the best accuracy in image recognition task with varying degrees of reduced communication costs compared to the three baseline methods.",
keywords = "Federated learning, Network pruning, Private preserving",
author = "Yang Liu and Yi Zhao and Guangmeng Zhou and Ke Xu",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",
year = "2021",
doi = "10.1007/978-3-030-92307-5\_50",
language = "English",
isbn = "9783030923068",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "430--437",
editor = "Teddy Mantoro and Minho Lee and Ayu, \{Media Anugerah\} and Wong, \{Kok Wai\} and Hidayanto, \{Achmad Nizar\}",
booktitle = "Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings",
address = "Germany",
}