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: over 1 year ago Related projects:
| Repository | Description | Stars |
|---|---|---|
| mingruiliu-ml-lab/episode_plusplus | An algorithm for Federated Learning that handles client subsampling and data heterogeneity with unbounded smoothness | 0 |
 lins-lab/fedbr | An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data | 25 |
 illidanlab/splitmix | An algorithm for distributed learning with flexible model customization during training and testing | 40 |
 hongliny/fedac-neurips20 | Provides code for a federated learning algorithm to optimize machine learning models in a distributed setting. | 14 |
 litian96/fedprox | An optimization framework designed to address heterogeneity in federated learning across distributed networks | 643 |
 optimization-ai/icml2023_fedxl | An implementation of a federated learning algorithm for optimization problems with compositional pairwise risk optimization. | 2 |
 baowenxuan/fedcollab | An algorithm that optimizes collaboration in federated learning by clustering clients into non-overlapping coalitions based on data quantity and pairwise distribution distances. | 16 |
 divyansh03/fedexp | An implementation of a federated averaging algorithm with an extrapolation approach to speed up distributed machine learning training on client-held data. | 9 |
 zackzikaixiao/fedgrab | A tool for training federated learning models with adaptive gradient balancing to handle class imbalance in multi-client scenarios. | 13 |
 hmgxr128/mifa_code | An implementation of Fast Federated Learning under device unavailability for minimizing latency and achieving optimal convergence rates | 9 |
 mediabrain-sjtu/pfedgraph | 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 |
 harliwu/fedamd | This project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence. | 2 |
 pengyang7881187/fedrl | Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 54 |
 omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 154 |
| hongliny/fco-icml21 | This code repository provides an implementation of Federated Composite Optimization for decentralized machine learning | 11 |