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Li, Zonghang's Projects

metisfl icon 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.

mxnet-memonger icon mxnet-memonger

Sublinear memory optimization for deep learning, reduce GPU memory cost to train deeper nets

netstorm icon netstorm

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

persf-semcom icon persf-semcom

PerSF-SemCom: Personalized Saliency-Based Semantic Communication

pfl-non-iid icon 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 icon ps-lite

A lightweight parameter server interface

pso-mlp icon pso-mlp

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

pyfirewall icon pyfirewall

Python控制iptables的自动隔离爬虫和端口扫描的防火墙

pytorch icon pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

resnet icon resnet

Reproduce ResNet-v2(Identity Mappings in Deep Residual Networks) with MXNet

sfc-dqn icon sfc-dqn

Tensorflow Implementation of SFC-DQN

singa icon singa

a distributed deep learning platform

tsp icon tsp

遗传算法、禁忌搜索、模拟退火、蚁群算法

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