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UVeQFed: Universal Vector Quantization for Federated Learning
Nir Shlezinger
, Mingzhe Chen
, Yonina C. Eldar
, H. Vincent Poor
, Shuguang Cui
Department of Electrical & Computer Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
237
Scopus citations
Overview
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Dive into the research topics of 'UVeQFed: Universal Vector Quantization for Federated Learning'. Together they form a unique fingerprint.
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Keyphrases
Federated Learning
100%
Vector Quantization
100%
Learning Model
60%
Training Model
40%
Numerical Results
20%
Loss Function
20%
Deep Learning Model
20%
Unique Characteristics
20%
Training System
20%
Global Model
20%
Emerging Approaches
20%
Private Information
20%
Number of Users
20%
Iterative Procedure
20%
Minimum Distortion
20%
Quantization Scheme
20%
Quantization Method
20%
Uplink Channel
20%
Federated Averaging
20%
Quantization Theory
20%
Decentralized Training
20%
Rate-constrained
20%
Centralized Server
20%
Constrained Channels
20%
Aggregated Model
20%
Computer Science
Vector Quantization
100%
Federated machine learning
100%
Deep Learning Model
20%
Minimum Distortion
20%