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Issues list

[WIP] A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks gTop-k S-SGD
#20 opened Jun 4, 2021 by mitsuhiko-nozawa
@mitsuhiko-nozawa
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees piplining Top-k S-SGD
#19 opened Jun 4, 2021 by mitsuhiko-nozawa
@mitsuhiko-nozawa
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization gradient quantization QSGD
#18 opened Jun 4, 2021 by ryoherisson
@ryoherisson
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks gradient quantization signSGD
#17 opened Jun 4, 2021 by mitsuhiko-nozawa
@mitsuhiko-nozawa
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training gradient quantization
#16 opened Jun 4, 2021 by ryoherisson
@ryoherisson
Quantized compressive sampling of stochastic gradients for efficient communication in distributed deep learning gradient quantization QSGD signSGD
#15 opened Jun 3, 2021 by mitsuhiko-nozawa
@mitsuhiko-nozawa
[WIP] Election coding for distributed learning: Protecting SignSGD against byzantine attacks signSGD
#14 opened Jun 2, 2021 by nocotan
@nocotan
Communication-Efficient Distributed Deep Learning with Merged Gradient Sparsification on GPUs piplining Top-k S-SGD
#13 opened Jun 1, 2021 by mitsuhiko-nozawa
@mitsuhiko-nozawa
Error Feedback Fixes SignSGD and other Gradient Compression Schemes gradient quantization QSGD signSGD theoretical analysis Top-k S-SGD
#12 opened Jun 1, 2021 by nocotan
@nocotan
signSGD with majority vote is communication efficient and fault tolerant gradient quantization signSGD
#11 opened May 31, 2021 by nocotan
@nocotan
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning gradient quantization TernGrad
#10 opened May 28, 2021 by nocotan
@nocotan
One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis federated learning QSGD
#9 opened May 28, 2021 by mitsuhiko-nozawa
@mitsuhiko-nozawa
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning gradient quantization QSGD theoretical analysis Top-k S-SGD
#8 opened May 26, 2021 by nocotan
@nocotan
Sparsified SGD with Memory MEM-SGD theoretical analysis Top-k S-SGD
#7 opened May 25, 2021 by nocotan
@nocotan
The Convergence of Sparsified Gradient Methods theoretical analysis Top-k S-SGD
#6 opened May 19, 2021 by nocotan
@nocotan
Asynchronous Distributed Learning with Sparse Communications and Identification asynchronous distributed learning theoretical analysis
#5 opened May 14, 2021 by mitsuhiko-nozawa
@mitsuhiko-nozawa
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding gradient quantization QSGD
#4 opened May 12, 2021 by ryoherisson
@ryoherisson
99% of Worker-Master Communication in Distributed Optimization Is Not Needed theoretical analysis
#3 opened May 11, 2021 by nocotan
@nocotan
A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification gTop-k S-SGD theoretical analysis Top-k S-SGD
#2 opened May 10, 2021 by nocotan
@nocotan
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training Top-k S-SGD
#1 opened May 7, 2021 by mitsuhiko-nozawa
@mitsuhiko-nozawa
ProTip! Exclude everything labeled bug with -label:bug.