There are 3 repositories under fedavg topic.
An open framework for Federated Learning.
Handy PyTorch implementation of Federated Learning (for your painless research)
PyTorch implementation of FedNova (NeurIPS 2020), and a class of federated learning algorithms, including FedAvg, FedProx.
Blades: A Unified Benchmark Suite for Byzantine Attacks and Defenses in Federated Learning
(NeurIPS 2022) Official Implementation of "Preservation of the Global Knowledge by Not-True Distillation 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
NAACL '24 (Best Demo Paper RunnerUp) / MlSys @ NeurIPS '23 - RedCoast: A Lightweight Tool to Automate Distributed Training and Inference
This repository contains all the implementation of different papers on Federated Learning
Three implementations of FedAvg: numpy, pytorch and tensorflow federated.
Apply Federated Learning and Deep Learning (Deep Auto-encoder) to detect abnormal data for IoT devices.
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
Simulate the fedavg and fedprox algorithm of federated learning
Centralized Federated Learning using WebSockets and TensorFlow
The implementation of FedAvg based on pytorch .
(CVPR 2024) Official Implementation of "FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning"
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
Hyper-tuned federated learning using flower framework.
Experiments of the FL in Healthcare project - MRI images use case - using Flower
federated learning standalone modeling environment
Using FedAvg method to predict future temperature.
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.