There are 3 repositories under fedavg topic.
An open framework for Federated Learning.
Handy PyTorch implementation of Federated Learning (for your painless research)
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
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
This repository contains all the implementation of different papers on Federated Learning
Three implementations of FedAvg: numpy, pytorch and tensorflow federated.
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.
Simple implementation of FedAvg, a Federated Learning algorithm.
Federated Learning Experiments for Remote Sensing image data using convolution neural networks
Centralized Federated Learning using WebSockets and TensorFlow
Simulate the fedavg and fedprox algorithm of federated learning
The implementation of FedAvg based on pytorch .
Federated Learning with flower and pytorch using a metaheuristic based on the beta distribution
Decentralized (P2P) Federated Learning implementation using libp2p JavaScript
A PyTorch framework for federated learning. This is a very basic framework.
Comparison of FedAvg and FedDyn as a final project for the Advance Machine Learning course at Politecnico di Torino
Federated Learning for Swarm Robotics
Experiments of the FL in Healthcare project - MRI images use case - using Flower
Comparing centralised machine learning and federated learning using flower framework. Building a custom strategy over the base FedAvg called FedCustom which has a higher learning rate and several other hyper parameters to increase the accuracy.
federated learning standalone modeling environment
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.
A classic implementation of Federated Learning for identifying FALL and ADL from images with Transfer Learning.
Using FedAvg method to predict future temperature.