Lizonghang

AGOD

AI-generated Optimization Decision

KlonetAI

KlonetAI: An AI agent for intelligent interaction with Klonet.

fedgs

FedGS: A Federated Group Synchronization Framework Implemented by LEAF-MX.

sfc-dqn

Tensorflow Implementation of SFC-DQN

PerSF-SemCom

PerSF-SemCom: Personalized Saliency-Based Semantic Communication

leaf-mx

LEAF-MX is a MXNET benchmark for federated learning.

FaceNetServer

Implement YOLOv2+MTCNN+FaceNet on Django Backend.

ESync

ESync: An Efficient Synchronous Algorithm for Distributed ML in Heterogeneous Clusters

lizonghang.github.io

Welcome to my homepage! Please see lizonghang.github.io for details.

Cupid

Implementation of paper Cupid and Dynamic Scheduling of Network Updates

leaf-torch

Hey,this is a torch implementation of LEAF(a benchmark of FL)

learn-gluon

code of gluonbook

MCF-Load-Balance

PSO-MLP

Train Multi-Layer Perception Model model via standard Particle Swarm Optimization.

GeoMX

GeoMX: Fast and unified distributed system for training machine learning algorithms over geographical data centers.

netstorm

NetStorm: Accelerating Geo-distributed Machine Learning with Network-Aware Adaptive Tree and Auxiliary Route

Symphonys

AutoGPT-Next-Web

🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.

CLRS

Python Implement Version of 《Introduction to Algorithm》

dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

flower

Flower - A Friendly Federated Learning Framework

incubator-mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

metisfl

MetisFL is a federated learning framework that allows developers to easily federate their machine learning workflows and train their models across distributed data silos without ever collecting the data in a centralized location. The core of the framework is written in C++ and focuses on scalability, speed and resiliency.

NBSync

overleaf

A web-based collaborative LaTeX editor

PFL-Non-IID

The origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to crack this hard nut. In contrast, the personalized federated learning may take the advantage of the Non-IID data to learn the personalized model for each user. Thanks to @Stonesjtu, this platform can also record the GPU memory usage for the model.

ps-lite

A lightweight parameter server interface

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

saliency_metrics

singa

a distributed deep learning platform