Federated-Minimax-and-Conditional-Stochastic-Optimization
Optimization framework
This project presents optimization techniques for federated learning and minimax games in the context of machine learning
1 stars
1 watching
0 forks
Language: Python
last commit: over 1 year ago Related projects:
Repository | Description | Stars |
---|---|---|
| An approach to train and optimize machine learning models in a decentralized setting by convexifying the optimization process | 4 |
| A framework for developing and testing decentralized machine learning algorithms | 2 |
| An approach to efficient federated learning by progressively training models on client devices with reduced communication and computation requirements. | 20 |
| This code repository provides an implementation of Federated Composite Optimization for decentralized machine learning | 12 |
| An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. | 2 |
| Improving generalization in federated learning by seeking flat minima through optimization techniques | 82 |
| An implementation of algorithms for nonconvex federated learning optimization | 8 |
| A collection of stochastic optimization algorithms for large-scale machine learning problems | 221 |
| An optimization framework designed to address heterogeneity in federated learning across distributed networks | 655 |
| An implementation of a federated optimization algorithm for distributed machine learning | 6 |
| This project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence. | 2 |
| An optimizer that automatically tunes momentum and learning rate using local quadratic approximation. | 287 |
| Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting. | 14 |
| A modular JAX implementation of federated learning via posterior averaging for decentralized optimization | 50 |
| An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance | 2 |