pFedGraph
Federated Learning
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 stars
0 watching
2 forks
Language: Python
last commit: over 1 year ago Related projects:
| Repository | Description | Stars |
|---|---|---|
| mediabrain-sjtu/fedgela | This project enables federated learning across partially class-disjoint data with curated bilateral curation. | 11 |
| mediabrain-sjtu/feddisco | A federated learning framework with discrepancy-aware collaboration for decentralized data training | 68 |
 dawenzi098/sfl-structural-federated-learning | A Python implementation of Personalized Federated Learning with Graph using PyTorch. | 49 |
 jinheonbaek/fed-pub | Personalized Subgraph Federated Learning framework for distributed machine learning | 45 |
 yuetan031/fedstar | This project implements a federated learning algorithm for non-IID graph classification tasks by leveraging structural knowledge sharing. | 60 |
 pengyang7881187/fedrl | Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 56 |
| yuetan031/fedproto | An implementation of federated learning with prototype-based methods across heterogeneous clients | 134 |
 idanachituve/pfedgp | An implementation of Personalized Federated Learning with Gaussian Processes using Python. | 32 |
 bdemo/pfedbred_public | A project that proposes a novel federated learning approach to address the issue of incomplete information in personalized machine learning models | 9 |
 sungwon-han/fedx | An unsupervised federated learning algorithm that uses cross knowledge distillation to learn meaningful data representations from local and global levels. | 69 |
 yamingguo98/fediir | An implementation of a federated learning algorithm that generalizes to out-of-distribution scenarios using implicit invariant relationships | 10 |
 omarfoq/fedem | Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
 jhoon-oh/fedbabu | An implementation of federated learning algorithm for image classification | 50 |
 lx10077/fedavgpy | The purpose of this project is to investigate the convergence of a federated learning algorithm on non-IID (non-identically and independently distributed) data. | 255 |
 xtra-computing/fedov | Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. | 17 |