DQ-SGD: Dynamic Quantization in SGD for Communication-Efficient Distributed Learning
Author:
Funder
City University of Hong Kong
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9637506/9637115/09637749.pdf?arnumber=9637749
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. Rate distortion optimization for adaptive gradient quantization in federated learning;Digital Communications and Networks;2024-02
2. Rate-Distortion Optimization for Adaptive Gradient Quantization in Federated Learning;2023 IEEE Wireless Communications and Networking Conference (WCNC);2023-03
3. AC-SGD: Adaptively Compressed SGD for Communication-Efficient Distributed Learning;IEEE Journal on Selected Areas in Communications;2022-09
4. Communication Efficient Federated Learning With Heterogeneous Structured Client Models;IEEE Transactions on Emerging Topics in Computational Intelligence;2022
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