EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. NeurIPS, 2022
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Updated
Jan 24, 2024 - Python
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. NeurIPS, 2022
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This repository contains the code that produces the numeric section in On the Use of TensorFlow Computation Graphs in combination with Distributed Optimization to Solve Large-Scale Convex Problems
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optopy is a prototyping and benchmarking Python framework for optimization, both static and dynamic, centralized and distributed
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This repo is an implementation of the algorithm from the paper Consensus on Lie groups for the Riemannian Center of Mass. This algorithm computes the Riemannian center of mass of a set of points in a distributed manner, generalizing the Euclidean average consensus dynamics.
Implementation of consensus algorithms using row-stochastic weights over directed graphs
We present an algorithm to dynamically adjust the data assigned for each worker at every epoch during the training in a heterogeneous cluster. We empirically evaluate the performance of the dynamic partitioning by training deep neural networks on the CIFAR10 dataset.
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Error feedback based quantization aided and convergence guaranteed Communication Efficient Federated Linear and Deep GCCA
Distributed approach of scheduling residential EV charging to maintain reliability of power distribution grids.
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