Handy PyTorch implementation of Federated Learning (for your painless research)

A simulation framework for Federated Learning written in PyTorch

🔨 A toolbox for federated learning, aiming to provide implementations of FedAvg, FedProx, Ditto, etc. in multiple versions, such as Pytorch/Tensorflow, single-machine/distributed, synchronized/asynchronous.

A pytorch implementation of the paper "Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage".

Simplified implementation of federated learning in PyTorch

This is an unofficial implementation of Federated Transfer Learning using UCI Smartphone dataset

This repository contains PyTorch implementation of the paper ''LFighter: Defending against Label-flipping Attacks in Federated Learning''.

PyTorch code for our paper "Resource-Adaptive Federated Learning with All-In-One Neural Composition" (NeurIPS2022)

PyTorch implementation of Joint Privacy Enhancement and Quantization in Federated Learning (IEEE TSP 2023, IEEE ICASSP 2023, IEEE ISIT 2022)

The official Pytorch implementation of paper "FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation" accepted by MICCAI 2023

A flexible framework for running experiments with PyTorch models in a simulated Federated Learning (FL) environment.

Easy-to-Use Federated Learning Simulator in Pytorch

This repository is an official PyTorch implementation of paper: Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data. CVPR 2024.

Pytorch implementation of FedTAN (federated learning algorithm tailored for batch normalization) proposed in the paper, Why Batch Normalization Damage Federated Learning on Non-IID Data.

A federated learning simulator written in PyTorch, which supports a virtually unlimited number of clients.

CICModBus2023 pcap dataset flow extraction , Labeling, Train AE,VAE and AAE models (Pytorch) for Anomaly-based Network Inrusion Detection in a Federated Learning framework (flower))

This repository is PyTorch implementation for paper CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization which is accepted by CCGRID-23 Conference.

Device-to-Device (D2D) and assocaited Federated Learning simulation in Python using Pytorch for Learning operations. Implemented simultaneous operation of multiple FL modes.

This repository contains the official PyTorch code for the paper, Federated Learning via Meta-Variational Dropout published, in NeurIPS 2023

This repository contains PyTorch implementation of the paper Efficient Detection of Byzantine Attacks in Federated Learning Using Last Layer Biases

Framework for Federated Learning with FedAvg algorithm in PyTorch

Using Federated Learning to train the model in Semantic Communication in pytorch

An example of differential privacy with laplace mechanism in federated learning using Pytorch

Pytorch Implementation of FedRIR: Rethinking Information Representation in Federated Learning (Accepted by WWW25, Oral)

Get started with Flower Welcome to the Flower federated learning tutorial! In this notebook, we’ll build a federated learning system using Flower and PyTorch. In part 1, we use PyTorch for the model training pipeline and data loading. In part 2, we continue to federate the PyTorch-based pipeline using Flower.

In this project, a federated learning PyTorch setup has been deployed with differential privacy to analyse the privacy constraints within this setting.

A distributed machine learning framework designed for real-world edge deployments where devices are resource-constrained, connections are unreliable and trust cannot be assumed. Built in Rust from the ground up, it brings swarm intelligence and federated learning primitives to environments where PyTorch and TensorFlow cannot operate.

medical data used to perform federated learning using pytorch and syft. Differential privacy used to secure the data .Performed on the text data and also on image data .Datasets are listed in the pdf attached .A study of how well the algorithms are able to work with increasing the number of clients is also performed

An advanced, object-oriented framework in PyTorch for creating and comparing Federated Learning algorithms.

Simulation framework for Federated Learning (FedProx and FedAvg) in PyTorch.