[ICLR 2018] Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

[CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long*, Luka Rimanic, Ce Zhang, Bo Li

vector quantization for stochastic gradient descent.

Simple Implementation of the CVPR 2024 Paper "JointSQ: Joint Sparsification-Quantization for Distributed Learning"

We present a set of all-reduce compatible gradient compression algorithms which significantly reduce the communication overhead while maintaining the performance of vanilla SGD. We empirically evaluate the performance of the compression methods by training deep neural networks on the CIFAR10 dataset.

Geometric median (GM) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakdown point of 0.5. However, its computational complexity makes it infeasible for robustifying stochastic gradient descent (SGD) for high-dimensional optimization problems. In th…

😂Distributed optimizer implemented with TensorFlow MPI operation