episode
Federated Learning optimizer
An algorithm for Federated Learning with heterogeneous data, designed to optimize deep neural networks and improve performance
[ICLR 2023] EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
2 stars
1 watching
0 forks
Language: Python
last commit: almost 2 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| An algorithm for Federated Learning that handles client subsampling and data heterogeneity with unbounded smoothness | 0 |
| An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data | 25 |
| An algorithm for distributed learning with flexible model customization during training and testing | 40 |
| Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting. | 14 |
| An optimization framework designed to address heterogeneity in federated learning across distributed networks | 655 |
| An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. | 2 |
| An algorithm that optimizes collaboration in federated learning by clustering clients into non-overlapping coalitions based on data quantity and pairwise distribution distances. | 16 |
| An implementation of a federated averaging algorithm with an extrapolation approach to speed up distributed machine learning training on client-held data. | 9 |
| A tool for training federated learning models with adaptive gradient balancing to handle class imbalance in multi-client scenarios. | 14 |
| An implementation of Fast Federated Learning under device unavailability for minimizing latency and achieving optimal convergence rates | 9 |
| This project enables personalized federated learning with inferred collaboration graphs to improve the performance of machine learning models on non-IID (non-independent and identically distributed) datasets. | 26 |
| This project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence. | 2 |
| Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 56 |
| Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
| This code repository provides an implementation of Federated Composite Optimization for decentralized machine learning | 12 |