fedprox

There are 0 repository under fedprox topic.

securefederatedai / openfl
An open framework for Federated Learning.
federated-learning secure-computation openfl distributed-computing deep-learning collaborative-learning privacy-preserving-machine-learning machine-learning fedprox fedavg federated-analytics python federated-learning-framework federated-deep-learning pytorch-federated-learning tensorflow-federated-learning keras-federated-learning distributed-learning fedcurv fedopt
Language:Jupyter Notebook 665
vaseline555 / Federated-Learning-in-PyTorch
Handy PyTorch implementation of Federated Learning (for your painless research)
federated-learning pytorch fedavg fedprox fedsgd leaf-benchmark deep-learning fedadagrad fedadam fedopt fedyogi
Language:Python 367
ki-ljl / FedProx-PyTorch
PyTorch implementation of FedProx (Federated Optimization for Heterogeneous Networks, MLSys 2020).
federated-learning fedprox pytorch
Language:Python 86
Lee-Gihun / FedNTD
(NeurIPS 2022) Official Implementation of "Preservation of the Global Knowledge by Not-True Distillation in Federated Learning"
artificial-intelligence computer-vision continual-learning deep-learning deep-neural-networks distributed-learning fedavg federated-learning knowledge-distillation machine-learning fedavgm fedcurv feddyn fedprox moon scaffold fednova fedntd
Language:Python 69
c-gabri / Federated-Learning-PyTorch
PyTorch implementation of Federated Learning algorithms FedSGD, FedAvg, FedAvgM, FedIR, FedVC, FedProx and standard SGD, applied to visual classification. Client distributions are synthesized with arbitrary non-identicalness and imbalance (Dirichlet priors). Client systems can be arbitrarily heterogeneous. Several mobile-friendly models are provided
federated-learning fedsgd fedavg fedavgm fedir fedvc fedprox non-iid imbalance heterogeneity classification pytorch sgd
Language:Python 62
ayushm-agrawal / Federated-Learning-Implementations
This repository contains all the implementation of different papers on Federated Learning
federated-learning pytorch pytorch-implementation privacy-preserving-machine-learning fedavg fedprox
Language:Jupyter Notebook 43
ysyisyourbrother / Federated-Learning-Research
An implementation of federated learning research baseline methods based on FedML-core, which can be deployed on real distributed cluster and help researchers to explore more problems existing in real FL systems.
distributed-systems fedavg federated-learning fedprox framework fedasync fedat fedml eco-fl
Language:Python 23
shyam671 / Federated-Learning
fedavg federated federated-learning fedprox moon
Language:Jupyter Notebook 16
anandcu3 / Federated-Learning-for-Remote-Sensing
Federated Learning Experiments for Remote Sensing image data using convolution neural networks
bcp convolutional-neural-networks fedavg federated-learning remote-sensing fedprox
Language:Jupyter Notebook 11
Turningl / fedavg_pytorch
Simulate the fedavg and fedprox algorithm of federated learning
dpsgd federated-learning opacus pytorch fedavg fedprox
Language:Python 8
BThameur / FL-for-Smart-Healthcare
Experiments of the FL in Healthcare project - MRI images use case - using Flower
fedavg federated-learning fedprox flwr healthcare mri-images fedadagrad fedadam
Language:Jupyter Notebook 1
gautamHCSCV / Federated-Learning-Methods-Comparison
We utilize the Adversarial Model Perturbations (AMP) regularizer to regularize clients’ models. The AMP regulzaizer is based on perturbing the model parameters so as to get a more generalized model. The claim of AMP regularizer is to reach flat minima and therefore is expected to reach flat minima in FL settings as well.
federated-learning fedprox pytorch fedavg fedntd scaffold amp-regularizer
Language:Python 0

fedprox

securefederatedai / openfl

vaseline555 / Federated-Learning-in-PyTorch

ki-ljl / FedProx-PyTorch

Lee-Gihun / FedNTD

c-gabri / Federated-Learning-PyTorch

ayushm-agrawal / Federated-Learning-Implementations

ysyisyourbrother / Federated-Learning-Research

shyam671 / Federated-Learning

anandcu3 / Federated-Learning-for-Remote-Sensing

Turningl / fedavg_pytorch

BThameur / FL-for-Smart-Healthcare

gautamHCSCV / Federated-Learning-Methods-Comparison