Issues · CompML/survey-deep-gradient-compression · GitHub

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[WIP] A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks

#20

· mitsuhiko-nozawa opened

on Jun 4, 2021

Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees

#19

· mitsuhiko-nozawa opened

on Jun 4, 2021

Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization

gradient quantization

#18

· ryoherisson opened

on Jun 4, 2021

Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks

gradient quantization

#17

· mitsuhiko-nozawa opened

on Jun 4, 2021

GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training

gradient quantization

#16

· ryoherisson opened

on Jun 4, 2021

Quantized compressive sampling of stochastic gradients for efficient communication in distributed deep learning

gradient quantization

#15

· mitsuhiko-nozawa opened

on Jun 3, 2021

[WIP] Election coding for distributed learning: Protecting SignSGD against byzantine attacks

#14

· nocotan opened

on Jun 2, 2021

Communication-Efficient Distributed Deep Learning with Merged Gradient Sparsification on GPUs

#13

· mitsuhiko-nozawa opened

on Jun 1, 2021

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

gradient quantization

theoretical analysis

#12

· nocotan opened

on Jun 1, 2021

signSGD with majority vote is communication efficient and fault tolerant

gradient quantization

#11

· nocotan opened

on May 31, 2021

TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning

gradient quantization

#10

· nocotan opened

on May 28, 2021

One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis

federated learning

#9

· mitsuhiko-nozawa opened

on May 28, 2021