Code for ''Distributed Online Optimization with Coupled Inequality Constraints over Unbalanced Directed Networks'' (CDC 2023)
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Updated
Oct 31, 2023 - Python
Code for ''Distributed Online Optimization with Coupled Inequality Constraints over Unbalanced Directed Networks'' (CDC 2023)
Code for "A Distributed Buffering Drift-Plus-Penalty Algorithm for Coupling Constrained Optimization" (L-CSS, status: revise and resubmit)
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
Pomodoro: Progressive Decomposition Methods with Acceleration
Parallel optimizer based on the Global Function Search
dccp is a simple python package that implements DiPOA algorithm.
Error feedback based quantization aided and convergence guaranteed Communication Efficient Federated Linear and Deep GCCA
Consensus-ADMM for multi-robot trajectory optimization.
Error feedback based quantization aided and convergence guaranteed Communication Efficient Federated Linear and Deep GCCA
The repository focuses on conducting Federated Learning experiments using the Intel OpenFL framework with diverse machine learning models, utilizing image and tabular datasets, applicable different domains like medicine, banking etc.
Decentralized Sporadic Federated Learning: A Unified Methodology with Generalized Convergence Guarantees
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.
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. NeurIPS, 2022
Implementation of Local Updates Periodic Averaging (LUPA) SGD
Implementation of consensus algorithms using row-stochastic weights over directed graphs
Implemented FedAvg & FedProx: Decentralized Optimization Algorithms for Neural Networks for an Image Classification Task- Distributed Optimization and Learning(DOL) Course Project
optopy is a prototyping and benchmarking Python framework for optimization, both static and dynamic, centralized and distributed
We present UDP-based aggregation algorithms for federated learning. We also present a scalable framework for practical federated learning. We empirically evaluate the performance by training deep convolutional neural networks on the MNIST dataset and the CIFAR10 dataset.
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