Towards Dynamic Resource Allocation and Client Scheduling in
Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning
Approach

Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach

21 June 2024

Xin Wang

Shugong Xu

Papers citing "Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach"

8 / 8 papers shown

Title
Machine Learning Model Sizes and the Parameter Gap Pablo Villalobos J. Sevilla T. Besiroglu Lennart Heim A. Ho Marius Hobbhahn ALM ELM AI4CE 47 59 0 05 Jul 2022
Context-Aware Online Client Selection for Hierarchical Federated Learning Zhe Qu Rui Duan Lixing Chen Jie Xu Zhuo Lu Yao-Hong Liu 53 61 0 02 Dec 2021
Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data A. Abdellatif N. Mhaisen Amr M. Mohamed A. Erbad Mohsen Guizani Z. Dawy W. Nasreddine FedML 73 94 0 14 Jul 2021
Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications Shuyan Hu Xiaojing Chen Wei Ni Ekram Hossain Xin Wang AI4CE 67 114 0 02 Dec 2020
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation Canh T. Dinh N. H. Tran Minh N. H. Nguyen Choong Seon Hong Wei Bao Albert Y. Zomaya Vincent Gramoli FedML 91 333 0 29 Oct 2019
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks Mingzhe Chen Zhaohui Yang Walid Saad Changchuan Yin H. Vincent Poor Shuguang Cui FedML 61 1,181 0 17 Sep 2019
Client-Edge-Cloud Hierarchical Federated Learning Lumin Liu Jun Zhang S. H. Song Khaled B. Letaief FedML 53 736 0 16 May 2019
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 267 4,620 0 18 Oct 2016