omarfoq

Abnormal_Action_Detection

Character-Based-Text-Generation

communication-in-cross-silo-fl

Official code for "Throughput-Optimal Topology Design for Cross-Silo Federated Learning" (NeurIPS'20)

FedEM

Official code for "Federated Multi-Task Learning under a Mixture of Distributions" (NeurIPS'21)

FLSim

Federated Learning Simulator (FLSim) is a flexible, standalone core library that simulates FL settings with a minimal, easy-to-use API. FLSim is domain-agnostic and accommodates many use cases such as vision and text.

knn-per

Official code for "Personalized Federated Learning through Local Memorization" (ICML'22)

Pedestrian_Detection

Implementation of Faster r-cnn on Penn-Fudan database

streaming-fl

Official code for "Federated Learning for Data Streams" (AISTATS'23)

unsupervised_translator

Wasserstein-GAN

In this repository we explore Wasserstein gan with different archtectures and we apply them on different datasets

omarfoq/FedEM

Official code for "Federated Multi-Task Learning under a Mixture of Distributions" (NeurIPS'21)

omarfoq/knn-per

Official code for "Personalized Federated Learning through Local Memorization" (ICML'22)

omarfoq/communication-in-cross-silo-fl

Official code for "Throughput-Optimal Topology Design for Cross-Silo Federated Learning" (NeurIPS'20)

omarfoq/Pedestrian_Detection

Implementation of Faster r-cnn on Penn-Fudan database

omarfoq/streaming-fl

Official code for "Federated Learning for Data Streams" (AISTATS'23)

omarfoq/Abnormal_Action_Detection

omarfoq/unsupervised_translator

omarfoq/Character-Based-Text-Generation

omarfoq/FLSim

Federated Learning Simulator (FLSim) is a flexible, standalone core library that simulates FL settings with a minimal, easy-to-use API. FLSim is domain-agnostic and accommodates many use cases such as vision and text.

omarfoq/Wasserstein-GAN

In this repository we explore Wasserstein gan with different archtectures and we apply them on different datasets

omarfoq/Counting-Bloom-filter

omarfoq/EdgeML

This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.

omarfoq/FedProx

Federated Optimization in Heterogeneous Networks (MLSys '20)

omarfoq/GAN-comparison

omarfoq/Kernel_Methods_for_Machine_Learning

The goal of the data challenge is to learn how to implement machine learning algorithms, gain understanding about them and adapt them to structural data. For this reason, we have chosen a sequence classification task: predicting whether a DNA sequence region is binding site to a specific transcription factor.

omarfoq/leaf

Leaf: A Benchmark for Federated Settings

omarfoq/named-entity-recognition

omarfoq/Neural_Nets_from_Scratch

Implementation of shallow dense neural networks from scratch

omarfoq/omarfoq.github.io

omarfoq/Ordored-Rules-for-Classification

omarfoq/pFedHN

Official code implementation for "Personalized Federated Learning using Hypernetworks"

omarfoq/releasing-research-code

Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)