EGC: entropy-based gradient compression for distributed deep learning

DOI10.1016/J.INS.2020.05.121zbMath1475.68326OpenAlexW3092359860MaRDI QIDQ2056378

Di Kuang, Yuan Mei, Weigang Wu, Danyang Xiao, Mengqiang Chen, Binbin Guo

Publication date: 2 December 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.05.121

zbMATH Keywords

entropyneural networksdeep learningdistributed traininggradient compression

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07)

Related Items (3)

A new algorithm for compression of partially commutative alphabets ⋮ Byzantine-robust variance-reduced federated learning over distributed non-i.i.d. data ⋮ RTGA: robust ternary gradients aggregation for federated learning

Uses Software

• Penn Treebank
• MNIST
• CIFAR
• ImageNet
• Find
• TernGrad
• GNMT
• HOGWILD
• GPipe

Cites Work

• Unnamed Item
• Performance Optimization Using Partitioned SpMV on GPUs and Multicore CPUs
• Scheduling Precedence Constrained Stochastic Tasks on Heterogeneous Cluster Systems
• A Novel Security-Driven Scheduling Algorithm for Precedence-Constrained Tasks in Heterogeneous Distributed Systems

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