ahmedcs

adaptive-federated-learning

Code for paper "Adaptive Federated Learning in Resource Constrained Edge Computing Systems"

grace

GRACE - GRAdient ComprEssion for distributed deep learning

HyGenICC

Hypervisor-based Generic Congestion Control for Data Centres

Practical_FL_Tutorial

Advancing Federated Learning in Practice: From Theory to Real-World Edge Applications

REFL

Resource Efficient Federated Learning

RWNDQ

RWNDQ is an Equal Share Allocation Switch Design for Data Centre Networks

SICC

SDN-based Incast Congestion Control for Data Centers

SIDCo

SIDCo is An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems

T-RACKs

Timely ACKs Retransmission for Data Centres

TCP_loss_monitor

TCP socket-level monitoring of socket events (i.e, Open, Close, CWND, Fast Retransmit, Retransmission Timeouts, .. etc)

ahmedcs/Compressed_SGD_PyTorch

Implementation of Compressed SGD with Compressed Gradients in Pytorch

ahmedcs/distributed_ml

Compression schemes for KAUST project

ahmedcs/arxiv-latex-cleaner

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

ahmedcs/communication-in-cross-silo-fl

ahmedcs/databox

Databox container manager and dashboard server

ahmedcs/DeeBERT

DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference

ahmedcs/deploy-MLmodels-on-iotedge

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

ahmedcs/DLSF

Deep Learning based Scheduler for Stochastic Fog-Cloud computing environments

ahmedcs/FATE

An Industrial Grade Federated Learning Framework

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

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

ahmedcs/Federated2Fog

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

ahmedcs/fedlearner

A multi-party collaborative machine learning framework

ahmedcs/FedML

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

ahmedcs/FedNova

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

ahmedcs/FL-optimal-client-sampling

ahmedcs/fl-text-models

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

ahmedcs/flash

ahmedcs/GFL

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

ahmedcs/GrouProx

ahmedcs/ifca

Codebase for An Efficient Framework for Clustered Federated Learning.

ahmedcs/learning-to-quantize

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

ahmedcs/missing-semester

The Missing Semester of Your CS Education 📚

ahmedcs/NIID-Bench

ahmedcs/olar-federated-learning

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

ahmedcs/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.

ahmedcs/pFedMe

Personalized Federated Learning using Pytorch (pFedMe)

ahmedcs/PoliFL

Policy-Based Federated Learning

ahmedcs/Pytorch_FedMD

ahmedcs/SplitEasy

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

ahmedcs/tf-federated-research

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