To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices-Reference-Cited by-同舟云学术

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To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE Global Communications Conference (GLOBECOM)
language:
Short-container-title:

Author:

Prakash Pavana¹,Ding Jiahao¹,Wu Maoqiang²,Shu Minglei³,Yu Rong²,Pan Miao¹

Affiliation:

1. University of Houston,Department of Electrical and Computer Engineering,Houston,TX,77204

2. School of Automation, Guangdong University of Technology,Guangzhou,China

3. Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences),Jinan,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9685019/9685006/09685793.pdf?arnumber=9685793

Reference15 articles.

1. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour;goyal;ArXiv Preprint,2017

2. FetchSGD: Communication-Efficient Federated Learning with Sketching;rothchild;Thirty-seventh International Conference on Machine Learning,2020

3. Power and Performance Characterization and Modeling of GPU-Accelerated Systems

4. To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices

5. Scaffold: Stochastic Controlled Averaging for Federated Learning;karimireddy;Thirty-seventh International Conference on Machine Learning,0

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Accelerating Hybrid Federated Learning Convergence Under Partial Participation;IEEE Transactions on Signal Processing;2024

2. Workie-Talkie: Accelerating Federated Learning by Overlapping Computing and Communications via Contrastive Regularization;2023 IEEE/CVF International Conference on Computer Vision (ICCV);2023-10-01

3. Merit: an on-demand IoT service delivery and resource scheduling scheme for federated learning and blockchain empowered 6G edge networks with reduced time and energy cost;International Journal of Ad Hoc and Ubiquitous Computing;2023

4. IoT Device Friendly and Communication-Efficient Federated Learning via Joint Model Pruning and Quantization;IEEE Internet of Things Journal;2022-08-01

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