Towards Energy Efficient Federated Learning over 5G+ Mobile Devices

13 January 2021

Papers citing "Towards Energy Efficient Federated Learning over 5G+ Mobile Devices"

7 / 7 papers shown

Title
Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments Pengcheng Sun Erwu Liu Wei Ni Kanglei Yu Rui Wang Abbas Jamalipour FedML 69 0 0 06 May 2025
To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices Liang Li Dian Shi Ronghui Hou Hui Li Miao Pan Zhu Han FedML 62 151 0 22 Dec 2020
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission Rui Chen Liang Li Kaiping Xue Chi Zhang Miao Pan Yuguang Fang MQ 52 40 0 21 Dec 2020
Federated Learning for Mobile Keyboard Prediction Andrew Straiton Hard Kanishka Rao Zhifeng Lin Swaroop Indra Ramaswamy Youjie Li S. Augenstein Alex Schwing M. Annavaram A. Avestimehr FedML 136 1,545 0 08 Nov 2018
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour Priya Goyal Piotr Dollár Ross B. Girshick P. Noordhuis Lukasz Wesolowski Aapo Kyrola Andrew Tulloch Yangqing Jia Kaiming He 3DH 128 3,685 0 08 Jun 2017
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,486 0 17 Feb 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 261 8,854 0 01 Oct 2015