Resource Rationing for Wireless Federated Learning: Concept, Benefits,
and Challenges

Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges

14 April 2021

Papers citing "Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges"

10 / 10 papers shown

Title
Federated Learning over Noisy Channels: Convergence Analysis and Design Examples Xizixiang Wei Cong Shen FedML 103 14 0 06 Jan 2021
Design and Analysis of Uplink and Downlink Communications for Federated Learning Sihui Zheng Cong Shen Xiang Chen 67 145 0 07 Dec 2020
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective Jie Xu Heqiang Wang 45 358 0 09 Apr 2020
Federated Learning over Wireless Fading Channels M. Amiri Deniz Gunduz 104 510 0 23 Jul 2019
Robust and Communication-Efficient Federated Learning from Non-IID Data Felix Sattler Simon Wiedemann K. Müller Wojciech Samek FedML 66 1,356 0 07 Mar 2019
Towards Federated Learning at Scale: System Design Keith Bonawitz Hubert Eichner W. Grieskamp Dzmitry Huba A. Ingerman ... H. B. McMahan Timon Van Overveldt David Petrou Daniel Ramage Jason Roselander FedML 121 2,664 0 04 Feb 2019
Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version) Guangxu Zhu Yong Wang Kaibin Huang FedML 67 643 0 30 Dec 2018
Towards an Intelligent Edge: Wireless Communication Meets Machine Learning Guangxu Zhu Dongzhu Liu Yuqing Du Changsheng You Jun Zhang Kaibin Huang 47 506 0 02 Sep 2018
Adaptive Federated Learning in Resource Constrained Edge Computing Systems Shiqiang Wang Tiffany Tuor Theodoros Salonidis K. Leung C. Makaya T. He Kevin S. Chan 242 1,706 0 14 Apr 2018
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 293 4,643 0 18 Oct 2016