A Comprehensive Survey On Client Selections in Federated Learning

12 November 2023

Papers citing "A Comprehensive Survey On Client Selections in Federated Learning"

9 / 9 papers shown

Title
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning Yae Jee Cho Samarth Gupta Gauri Joshi Osman Yağan FedML 59 69 0 14 Dec 2020
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies Yae Jee Cho Jianyu Wang Gauri Joshi FedML 143 408 0 03 Oct 2020
Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning Shaoxiong Ji Wenqi Jiang A. Walid Xue Li FedML 85 66 0 21 Mar 2020
Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism L. U. Khan Shashi Raj Pandey Nguyen H. Tran Walid Saad Zhu Han Minh N. H. Nguyen Choong Seon Hong FedML 55 385 0 06 Nov 2019
Active Federated Learning Jack Goetz Kshitiz Malik D. Bui Seungwhan Moon Honglei Liu Anuj Kumar FedML 45 137 0 27 Sep 2019
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates Dong Yin Yudong Chen Kannan Ramchandran Peter L. Bartlett OOD FedML 118 1,500 0 05 Mar 2018
Generalized Byzantine-tolerant SGD Cong Xie Oluwasanmi Koyejo Indranil Gupta AAML 73 258 0 27 Feb 2018
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,468 0 17 Feb 2016
Deep Reinforcement Learning with Double Q-learning H. V. Hasselt A. Guez David Silver OffRL 167 7,641 0 22 Sep 2015