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202205_reactadmin

React后台管理

cms_community_e_commerce

云生活超市后台管理系统——React后台项目：react + react-router4 + redux + antd + axios + sass。 （另外配套两个仓库：Spring Boot服务端、 React Native App端）

ddlo

Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks

deep-q-learning-for-mobile-edge-computing

drl-networking

Research on incentive mechanism design in mobile crowdsensing and mobile edge computing by deep reinforcement learning approaches.

droo

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

dynamic-request-scheduling-optimization-in-mobile-edge-computing-for-iot-applications

This project is the implementation of the research paper titled "Dynamic Request Scheduling Optimization in Mobile Edge Computing for IoT Applications"

ects

edge computing task schedule

edge-computing

Awesome-EdgeComputing：边缘计算技术指南和资源列表

edge-computing-codes

Simulation of algorithms for Edge Computing-related papers.

exercise

Exercise

front-end-interview-notebook

:ant:前端面试复习笔记

gao

halo

✍ An excellent open source blog publishing application. | 一个优秀的开源博客发布应用。

lydroo

Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks

mecoptimaloffloading

Optimization of Offloading Scheme Algorithm for Large Number of Tasks in Mobile-Edge Computing

mini-vue

实现最简 vue3 模型( Help you learn more efficiently vue3 source code )

project-ideas

集合优质的项目仓库，找到适合你的项目。

react-cart

React Hook + TypeScript 深入浅出实现一个购物车（性能优化、闭包陷阱、自定义hook）

react-tesla-shop

resources-allocation-in-the-edge-computing-environment-using-reinforcement-learning

Simulated the scenario between edge servers and users with a clear graphic interface. Also, implemented the continuous control with Deep Deterministic Policy Gradient (DDPG) to determine the resources allocation (offload targets, computational resources, migration bandwidth) in the edge servers

rl-for-binary-computation-offloading-in-wireless-powered-mec-networks

It's a implementation about the paper Liang Huang, Suzhi Bi, and Ying-jun Angela Zhang, "Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks", on https://ieeexplore.ieee.org/document/8771176

ssm

手把手教你整合最优雅SSM框架：SpringMVC + Spring + MyBatis

task-offloading-edge-computing

Simulation code for multi-user offloading in edge computing newtorks

uav-ddpg

Code for paper "Computation Offloading Optimization for UAV-assisted Mobile Edge Computing: A Deep Deterministic Policy Gradient Approach"

vhr

微人事是一个前后端分离的人力资源管理系统，项目采用SpringBoot+Vue开发。

vue-admin

webserver

A C++ High Performance Web Server

yj4889-optimized-quantization-for-convolutional-deep-neural-networks-in-federated-learning

Federated learning is a distributed learning method that trains a deep network on user devices without collecting data from central server. It is useful when the central server can’t collect data. However, the absence of data on central server means that deep network compression using data is not possible. Deep network compression is very important because it enables inference even on device with low capacity. In this paper, we proposed a new quantization method that significantly reduces FPROPS(floating-point operations per second) in deep networks without leaking user data in federated learning. Quantization parameters are trained by general learning loss, and updated simultaneously with weight. We call this method as OQFL(Optimized Quantization in Federated Learning). OQFL is a method of learning deep networks and quantization while maintaining security in a distributed network environment including edge computing. We introduce the OQFL method and simulate it in various Convolutional deep neural networks. We shows that OQFL is possible in most representative convolutional deep neural network. Surprisingly, OQFL(4bits) can preserve the accuracy of conventional federated learning(32bits) in test dataset.