ahmedcs

Compressed_SGD_PyTorch

Implementation of Compressed SGD with Compressed Gradients in Pytorch

distributed_ml

Compression schemes for KAUST project

arxiv-latex-cleaner

arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv

communication-in-cross-silo-fl

databox

Databox container manager and dashboard server

DeeBERT

DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

deploy-MLmodels-on-iotedge

This repository shows how to deploy machine learning models on Azure IoT Edge.

DLSF

Deep Learning based Scheduler for Stochastic Fog-Cloud computing environments

FATE

An Industrial Grade Federated Learning Framework

Federated-Learning-and-Split-Learning-with-raspberry-pi

SRDS 2020: End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things

Federated2Fog

Multi-Stage Hybrid Federated Learning over Large-Scale Wireless Fog Networks

fedlearner

A multi-party collaborative machine learning framework

FedML

(PyTorch > 1.0) A Research-Oriented Federated Learning Library. Supporting distributed computing, mobile/IoT on-device training, and standalone simulation

FedNova

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

FL-optimal-client-sampling

fl-text-models

Federated learning with text DNNs for DATA 591 at University of Washington.

flash

GFL

Galaxy Federated Learning Framework （星际联邦学习框架）

GrouProx

ifca

Codebase for An Efficient Framework for Clustered Federated Learning.

learning-to-quantize

Code for "Adaptive Gradient Quantization for Data-Parallel SGD", published in NeurIPS 2020.

missing-semester

The Missing Semester of Your CS Education 📚

NIID-Bench

olar-federated-learning

OLAR is an algorithm for optimal task assignment in the context of heterogeneous Federated Learning devices.

On_simulating_energy_consumption_of_federated_learning_systems

A simulation of energy consumption of a federated learning system based on the non-orthogonal multiple access (NOMA) transmission protocols, proposed by Mo et. al. Energy consumption is computed during training along with energy consumed by communications between local machines and the server.

pFedMe

Personalized Federated Learning using Pytorch (pFedMe)

PoliFL

Policy-Based Federated Learning

Pytorch_FedMD

SplitEasy

Code for "SplitEasy: A Practical Approach for Training ML models on Mobile Devices in a split second"

tf-federated-research

A collection of Google research projects related to Federated Learning and Federated Analytics.