fl-arbitrary-participation
FL analysis
Analyzes Federated Learning with Arbitrary Client Participation using various optimization strategies and datasets.
Code for paper "A Unified Analysis of Federated Learning with Arbitrary Client Participation"
4 stars
4 watching
3 forks
Language: Python
last commit: over 3 years agoRelated 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 |
 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 |
 omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
| ibm/reprogrammble-fl | Improves utility-privacy tradeoff in federated learning by reprogramming models to balance data utility and user privacy. | 5 |
 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 |
 symbioticlab/fedscale | A federated learning platform with tools and datasets for scalable and extensible machine learning experimentation | 390 |
 sarapieri/fed_het | This project investigates how to design architectures that enable better performance in federated learning systems, particularly for visual recognition tasks. | 10 |
 dawenzi098/sfl-structural-federated-learning | A Python implementation of Personalized Federated Learning with Graph using PyTorch. | 49 |
 zfancy/sfat | Combating heterogeneity in federated learning by combining adversarial training with client-wise slack during aggregation | 28 |
| ibm/federated-learning-lib | A framework for collaborative distributed machine learning in enterprise environments. | 500 |
 substra/substra | Enables the training and validation of machine learning models on distributed datasets in a secure and scalable manner. | 274 |
 lyn1874/fedpvr | An implementation of a federated learning algorithm for handling heterogeneous data | 6 |
 cuis15/fcfl | An implementation of Fair and Consistent Federated Learning using Python. | 20 |
 shams-sam/fedoptim | An open-source project exploring Federated Learning model updates and their rank structure using data from various datasets. | 14 |
 l3030/delta_fl | An implementation of an unbiased Federated Learning sampling scheme designed to improve model convergence and reduce variance in client participation. | 5 |