A PyTorch Implementation of Federated Learning

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.

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

Implementation of dp-based federated learning framework using PyTorch

Personalized Federated Learning with Moreau Envelopes (pFedMe) using Pytorch (NeurIPS 2020)

PyTorch Federated Learning (easy to use and extend)

PyTorch implementation of FedNova (NeurIPS 2020), and a class of federated learning algorithms, including FedAvg, FedProx.

A simulation framework for Federated Learning written in PyTorch

FedTorch is a generic repository for benchmarking different federated and distributed learning algorithms using PyTorch Distributed API.

PyTorch implementation of Federated Learning with Non-IID Data, and federated learning algorithms, including FedAvg, FedProx.

PyTorch implementation of Layer-wised Model Aggregation for Personalized 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

Unofficial Pytorch implementation of "Federated Meta-Learning with Fast Convergence and Efficient Communication"

PyTorch implementation of FedPer (Federated Learning with Personalization Layers).

PyTorch implementation of Per-FedAvg (Personalized Federated Learning: A Meta-Learning Approach).

PyTorch implementation of SCAFFOLD (Stochastic Controlled Averaging for Federated Learning, ICML 2020).

🔨 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".

PyTorch Implementation of Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach

Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.

A Python library for rapid prototyping, experimenting, and logging of federated learning using state-of-the-art models and datasets. Built using PyTorch and PyTorch Lightning.

PyTorch implementation of: D. Shenaj, M. Toldo, A. Rigon and P. Zanuttigh, “Asynchronous Federated Continual Learning”, CVPR 2023 Workshop on Federated Learning for Computer Vision (FedVision).

Federated learning with PyTorch (federated averaging and consensus optimization): with 'reduced' bandwidth

No description available

FL_PyTorch: Optimization Research Simulator for Federated Learning

Differential priavcy based federated learning framework by various neural networks and svm using PyTorch.

ByzFL: A Python library for robust federated learning, offering Byzantine-resilient aggregators, attack simulations, and ML pipelines for distributed systems. Compatible with PyTorch and NumPy.

Emotion detection from text using PyTorch and Federated Learning

PyTorch implementation of Security-Preserving Federated Learning via Byzantine-Sensitive Triplet Distance