Situation-Aware Cluster and Quantization Level Selection Algorithm for Fast Federated Learning-Reference-Cited by-同舟云学术

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Situation-Aware Cluster and Quantization Level Selection Algorithm for Fast Federated Learning

Published:2023-08-01 Issue:15 Volume:10 Page:13292-13302
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Seo Sangwon¹^ORCID,Lee Jaewook²,Ko Haneul³^ORCID,Pack Sangheon¹^ORCID

Affiliation:

1. School of Electrical Engineering, Korea University, Seoul, South Korea

2. Department of Information and Communication Engineering, Pukyong National University, Busan, South Korea

3. Department of Electronic Engineering, Kyung Hee University, Yongin, Gyeonggi, South Korea

Funder

National Research Foundation (NRF) of Korea

Korean Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/6488907/10194321/10083205.pdf?arnumber=10083205

Reference41 articles.

1. An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee

2. QPyTorch: A Low-Precision Arithmetic Simulation Framework

3. Survey of Personalization Techniques for Federated Learning

4. LEAF: A benchmark for federated settings;caldas;arXiv 1812 01097,2019

5. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

Cited by 2 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. Situation Awareness in AI-Based Technologies and Multimodal Systems: Architectures, Challenges and Applications;IEEE Access;2024

2. Wireless Federated Learning With Asynchronous and Quantized Updates;IEEE Communications Letters;2023-09

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