FedGS

Federated Learning Library

An implementation of a federated learning approach using graph-based sampling to handle arbitrary client availability in distributed machine learning

FedGS: Federated Graph-based Sampling with Arbitrary Client Availability, arxiv.org/abs/2211.13975) was accepted by AAAI 2023 Conference.

GitHub

16 stars
1 watching
4 forks
Language: Python
last commit: over 3 years ago

Related projects:

RepositoryDescriptionStars
mediabrain-sjtu/pfedgraphThis 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
wyjeong/fedmatchA project implementing Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning80
dawenzi098/sfl-structural-federated-learningA Python implementation of Personalized Federated Learning with Graph using PyTorch.49
idanachituve/pfedgpAn implementation of Personalized Federated Learning with Gaussian Processes using Python.32
tsingz0/fedalaAn implementation of a federated learning method for personalized models on non-iid datasets.116
yamingguo98/fediirAn implementation of a federated learning algorithm that generalizes to out-of-distribution scenarios using implicit invariant relationships10
harliwu/fedamdThis project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence.2
yuetan031/fedprotoAn implementation of federated learning with prototype-based methods across heterogeneous clients134
gaoliang13/feddcFederated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift81
haozzh/fedcrEvaluates various methods for federated learning on different models and tasks.19
yuetan031/fedstarThis project implements a federated learning algorithm for non-IID graph classification tasks by leveraging structural knowledge sharing.60
lx10077/fedavgpyThe purpose of this project is to investigate the convergence of a federated learning algorithm on non-IID (non-identically and independently distributed) data.255
zackzikaixiao/fedgrabA tool for training federated learning models with adaptive gradient balancing to handle class imbalance in multi-client scenarios.14
omarfoq/fedemDevelops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions.157
ignavierng/notears-admmAn implementation of Bayesian network structure learning with continuous optimization for federated learning.10