ZhaochengNiu

Active-Client-Selection-for-Communication-efficient-Federated-Learning

Active Client Selection for Federated Learning

AFLDDPG

AGOD

AI-generated Optimization Decision

Algorithm-for-DT-MEC-network-resource-allocation

ANS

Autodidactic Neurosurgeon Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning

Asynchronous-DRL-based-Multi-Hop-Task-Offloading-in-RSU-assisted-IoV-Networks

awesome-DeepLearning

深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI

awesome-federated-computing

:books: :eyeglasses: A collection of research papers, codes, tutorials and blogs on Federated Computing/Learning.

awesome-federated-learning

resources about federated learning and privacy in machine learning

MURIM

Multidimensional Reputation-based Incentive Mechanism for Federated Learning

ZhaochengNiu/FDDL

ZhaochengNiu/divfl

Diverse Client Selection for Federated Learning via Submodular Maximization

ZhaochengNiu/PyramidFL

[ACM MobiCom 2022] " PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning" by Chenning Li, Xiao Zeng, Mi Zhang, and Zhichao Cao.

ZhaochengNiu/FL_Incentive

Contribution Measurement Experiment for Federated Learning

ZhaochengNiu/MADDPG-ABS-Trajectory-Design

ZhaochengNiu/HCSFL

Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

ZhaochengNiu/ScaffoldFL

Implementation of Scaffold and Fedprox for Federated Learning using PyTorch

ZhaochengNiu/FBFL-A-Flexible-Blockchain-based-Federated-Learning-Framework-in-Mobile-Edge-Computing

We will implement this framework.

ZhaochengNiu/Cache-Allocation-Project

The purpose of this project is to implement machine learning methods to study resource allocation problems, that is how to share limited resources out among several agents.

ZhaochengNiu/maddpg_scut_err

UAVMADDPG

ZhaochengNiu/edgecloud_offloading

ZhaochengNiu/ECOS

Edge-based Computation Offloading Simulator

ZhaochengNiu/IoV-Computation-Offloading

ZhaochengNiu/flsim

A simulation framework for Federated Learning written in PyTorch

ZhaochengNiu/Learning-Based-Queuing-Delay-Aware-Task-Offloading-in-Collaborative-Vehicular-Networks

Evoluationay Algorithms Project: Learning-Based Queuing Delay-Aware Task Offloading in Collaborative Vehicular Networks

ZhaochengNiu/malfoy

ZhaochengNiu/Device-scheduling-for-federated-learning-over-recource-constrained-network

Device scheduling for federated learning over an recource constrained network.

ZhaochengNiu/MP-MAB

This project is created for the simulations of the paper: [Wang2021] Wenbo Wang, Amir Leshem, Dusit Niyato and Zhu Han, "Decentralized Learning for Channel Allocation inIoT Networks over Unlicensed Bandwidth as aContextual Multi-player Multi-armed Bandit Game", to appear in IEEE Transactions on Wireless Communications, 2021.

ZhaochengNiu/BLADE-FL

The code for the paper "Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation"

ZhaochengNiu/FEDL

Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation

ZhaochengNiu/ANS

Autodidactic Neurosurgeon Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning

ZhaochengNiu/FEEL

part code of paper entitled "battery-constrained federated edge learning in uav-enabled iot for b5g/6g networks"

ZhaochengNiu/Offloading-FL

Online data offloading for federated learning

ZhaochengNiu/WangYichi1-Computation-offloading-based-on-MADDPG

ZhaochengNiu/Optimization-and-Federated-Learning-for-Task-Offloading

ZhaochengNiu/Efficient-Client-Selection-in-Federated-Learning.

ZhaochengNiu/UnreliableClientsinFL

This code is related to the paper titled: Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design

ZhaochengNiu/Multi-Armed-Bandit-Based-Client-Scheduling-for-Federated-Learning

Code Implemntion from the article Multi-Armed Bandit Based Client Schedulingfor Federated Learning

ZhaochengNiu/Pytorch_FedMD

ZhaochengNiu/olar-federated-learning

OLAR is an algorithm for optimal task assignment in the context of heterogeneous Federated Learning devices.