Stars
FedScale is a scalable and extensible open-source federated learning (FL) platform.
PyTorch code for our paper "Resource-Adaptive Federated Learning with All-In-One Neural Composition" (NeurIPS2022)
1st place solution of ITU Graph Neural Networking Challenge 2023
A pytorch implementation of paper 'Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation', https://arxiv.org/abs/1905.08094
Collect awesome best papers from top AI conferences.
Hydra is a framework for elegantly configuring complex applications
A curated list to learn about distributed systems
An Emacs framework for the stubborn martian hacker
37 traditional FL (tFL) or personalized FL (pFL) algorithms, 3 scenarios, and 20 datasets.
A cheatsheet of modern C++ language and library features.
[NeurIPS 2019 FL workshop] Federated Learning with Local and Global Representations
[ICLR 2021] HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
This is the code repository for the following paper: "Model pruning enables efficient federated learning on edge devices".
[ICLR2022] Efficient Split-Mix federated learning for in-situ model customization during both training and testing time
[NeurIPS 2022] "FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction" by Samiul Alam, Luyang Liu, Ming Yan, and Mi Zhang
FedMD: Heterogenous Federated Learning via Model Distillation
Edge server, user dataset for Edge Computing research
CloudSim: A Framework For Modeling And Simulation Of Cloud Computing Infrastructures And Services
Eclipse SUMO is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks. It allows for intermodal simulation including pedestrians a…
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
Federated Optimization in Heterogeneous Networks (MLSys '20)
This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.
The offical notes of Andrew Ng Machine Learning in Stanford University