IoT Device Friendly and Communication-Efficient Federated Learning via Joint Model Pruning and Quantization-Reference-Cited by-同舟云学术

IoT Device Friendly and Communication-Efficient Federated Learning via Joint Model Pruning and Quantization

Published:2022-08-01 Issue:15 Volume:9 Page:13638-13650
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Prakash Pavana¹^ORCID,Ding Jiahao¹,Chen Rui¹^ORCID,Qin Xiaoqi²^ORCID,Shu Minglei³^ORCID,Cui Qimei²^ORCID,Guo Yuanxiong⁴^ORCID,Pan Miao¹^ORCID

Affiliation:

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

2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China

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

4. Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA

Funder

U.S. National Science Foundation

National Youth Top-Notch Talent Support Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/6488907/9839365/9691274-aam.pdf

Reference42 articles.

1. Automatic pruning for quantized neural networks;guerra;arXiv 2002 00523,2020

2. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding;han;Proc 4th Int Conf Learn Represent,2016

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

4. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data

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

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