yuyuyu13

AE-GAN

AE-GAN

APIJSONAuto

自动化接口管理工具，自动生成代码、自动静态检查、自动化回归测试、自动生成文档与注释等。

Articles

Documents and source code. About how to begin to learn algorithm and csharp. It is easy to understand if you have C/C++ or other programming language knowledge.

bkd_fedgnn

Implementation of paper "More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks"

Community-Detection-MTFL

🔬 本项目为SWPU2022届本科毕业设计《基于社区检测的多任务聚类联邦学习》。本研究提出了一种多任务聚类联邦学习的新方法，该方法的特点是基于社区检测来进行聚类簇的动态划分。

Computer_Graphics

华中科技大学《计算机图形学》MOOC课程实验 ——万琳老师

CTGAN

Conditional GAN for generating synthetic tabular data.

easyFL

An experimental platform to quickly realize and compare with popular centralized federated learning algorithms. A realization of federated learning algorithm on fairness (FedFV, Federated Learning with Fair Averaging, https://fanxlxmu.github.io/publication/ijcai2021/) was accepted by IJCAI-21 (https://www.ijcai.org/proceedings/2021/223).

EasyFL-1

An easy-to-use federated learning platform

weixin-java-tools

全能微信Java开发工具包，支持包括微信支付、开放平台、小程序、企业号和公众号等的开发

yuyuyu13/Computer_Graphics

华中科技大学《计算机图形学》MOOC课程实验 ——万琳老师

yuyuyu13/AE-GAN

AE-GAN

yuyuyu13/APIJSONAuto

自动化接口管理工具，自动生成代码、自动静态检查、自动化回归测试、自动生成文档与注释等。

yuyuyu13/bkd_fedgnn

Implementation of paper "More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks"

yuyuyu13/Community-Detection-MTFL

🔬 本项目为SWPU2022届本科毕业设计《基于社区检测的多任务聚类联邦学习》。本研究提出了一种多任务聚类联邦学习的新方法，该方法的特点是基于社区检测来进行聚类簇的动态划分。

yuyuyu13/CTGAN

Conditional GAN for generating synthetic tabular data.

yuyuyu13/easyFL

An experimental platform to quickly realize and compare with popular centralized federated learning algorithms. A realization of federated learning algorithm on fairness (FedFV, Federated Learning with Fair Averaging, https://fanxlxmu.github.io/publication/ijcai2021/) was accepted by IJCAI-21 (https://www.ijcai.org/proceedings/2021/223).

yuyuyu13/EasyFL-1

An easy-to-use federated learning platform

yuyuyu13/federated-learning

A PyTorch Implementation of Federated Learning http://doi.org/10.5281/zenodo.4321561

yuyuyu13/FedGS

FedGS: Federated Graph-based Sampling with Arbitrary Client Availability, arxiv.org/abs/2211.13975) was accepted by AAAI 2023 Conference.

yuyuyu13/FDARN

yuyuyu13/FedLab

A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.

yuyuyu13/FedLab-benchmarks

Standard federated learning implementations in FedLab and FL benchmarks.

yuyuyu13/FedMEMA

yuyuyu13/FedPerGNN

code of FedGNN

yuyuyu13/FEDXGBOOST-MPI4PY

Secured Federated Learning XGBoost - This project provides the implementation of FL XGBoost in python, which allows the fast prototyping of scecured protocols.

yuyuyu13/kfed

KFedExps -> kfed

yuyuyu13/Labs-Federated-Learning

Accenture Labs Federated Learning

yuyuyu13/MCKD

The code repository of MCKD model from paper (https://arxiv.org/pdf/2405.07155) "Enhancing Multi-modal Learning: Meta-learned Cross-modal Knowledge Distillation for Handling Missing Modalities".

yuyuyu13/MP-FedXGB

An Efficient Learning Framework For Federated XGBoostUsing Secret Sharing And Distributed Optimization

yuyuyu13/Multimodal-Federated-Learning-on-IoT-Data

yuyuyu13/NAIST-FedML-Experiments

yuyuyu13/PASSION

[ACM-MM'24 Oral] PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates

yuyuyu13/PDMINet

Prototype-Driven and Multi-Expert Integrated Multi-Modal MR Brain Tumor Image Segmentation

yuyuyu13/PFL-Non-IID

Personalized federated learning simulation platform with non-IID and unbalanced dataset

yuyuyu13/ProtoKD

[ICASSP 2023] Prototype Knowledge Distillation for Medical Segmentation with Missing Modality

yuyuyu13/REKCARC-TSC-UHT

清华大学计算机系课程攻略 Guidance for courses in Department of Computer Science and Technology, Tsinghua University

yuyuyu13/RFNet

code for our work: RFNet for Incomplete Multi-modal Brain Tumor Segmentation

yuyuyu13/secure-kmean-clustering

Practical Privacy-Preserving K-means Clustering (PETS-2020)

yuyuyu13/ShaSpec

The official code repository of ShaSpec model from CVPR 2023 [paper](https://arxiv.org/pdf/2307.14126) "Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling"