Joint Compression and Deadline Optimization for Communication-Efficient Federated Edge Learning
Author:
Affiliation:
1. College of Information Science and Electronic Engineering, Zhejiang University,Hangzhou,China
2. Shenzhen Research Institute of Big Data,Shenzhen,China
3. School of Computing Science, University of Glasgow
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10464388/10464393/10464818.pdf?arnumber=10464818
Reference14 articles.
1. signSGD: Compressed optimisation for non-convex problems;Bernstein
2. QSGD: Communication-efficient SGD via gradient quantization and encoding;Alistarh,2017
3. Deep gradient compression: Reducing the communication bandwidth for distributed training;Lin
4. Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization;Reisizadeh
5. Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning
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