An open framework for Federated Learning.
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Updated
Jun 5, 2024 - Jupyter Notebook
An open framework for Federated Learning.
Handy PyTorch implementation of Federated Learning (for your painless research)
Experiments of the FL in Healthcare project - MRI images use case - using Flower
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.
(NeurIPS 2022) Official Implementation of "Preservation of the Global Knowledge by Not-True Distillation in Federated Learning"
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.
PyTorch implementation of FedProx (Federated Optimization for Heterogeneous Networks, MLSys 2020).
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 prov…
Federated Learning Experiments for Remote Sensing image data using convolution neural networks
This repository contains all the implementation of different papers on Federated Learning
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