This repository contains all the implementation of different papers on Federated Learning
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Updated
Aug 12, 2020 - Jupyter Notebook
This repository contains all the implementation of different papers on Federated Learning
PyTorch implementation of FedNova (NeurIPS 2020), and a class of federated learning algorithms, including FedAvg, FedProx.
federated learning standalone modeling environment
Federated Learning Experiments for Remote Sensing image data using convolution neural networks
Centralized Federated Learning using WebSockets and TensorFlow
Comparison of FedAvg and FedDyn as a final project for the Advance Machine Learning course at Politecnico di Torino
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…
Decentralized (P2P) Federated Learning implementation using libp2p JavaScript
Federated Learning with flower and pytorch using a metaheuristic based on the beta distribution
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.
Apply Federated Learning and Deep Learning (Deep Auto-encoder) to detect abnormal data for IoT devices.
(NeurIPS 2022) Official Implementation of "Preservation of the Global Knowledge by Not-True Distillation in Federated Learning"
Federated Learning for Swarm Robotics
A Open Source Federated Learning Simulation Framework for Everyone
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.
Experiments of the FL in Healthcare project - MRI images use case - using Flower
Implementation of FedNCF with SecAvg
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