dangWV's starred repositories
Awesome-FL
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
FederatedScope
An easy-to-use federated learning platform
FedML
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.
GNNs-Recipe
🟠 A study guide to learn about Graph Neural Networks (GNNs)
net_for_Cryptocurrency
采用神经网络来预测加密货币涨跌,同时采用贝叶斯优化方法调整超参数
auto_modeling_result_visualizable
自动模型构筑自动超参数优化,可视化精度比较程序
federated-gcn
This repository contains python scripts related to a research that is intended to run graph convolution neural networks in federated manner on distributed graph databases
FedGraphNN
FedGraphNN: A Federated Learning Platform for Graph Neural Networks with MLOps Support. The previous research version is accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.
GraphNeuralNetwork
《深入浅出图神经网络:GNN原理解析》配套代码
GraphSAGE-Cora-Citeseer-Pubmed
这是GraphSAGE模型在Cora、Citeseer、Pubmed数据集上的复现代码。语言:PyTorch
GraphSAGE_pytorch
graphSAGE with pytorch
graph_nets
PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
graphSAGE-pytorch
A PyTorch implementation of GraphSAGE. This package contains a PyTorch implementation of GraphSAGE.
GCN-GAT-and-Graphsage
The code for GCN, GAT and Graphsage based on pytorch.
Pytorch_FL_GAN
Easy implementation of federated learning scenario on GAN tasks.
Pytorch_FL_CNN
An easy implementation of a federated learning scenario on CNN
communication-in-cross-silo-fl
Official code for "Throughput-Optimal Topology Design for Cross-Silo Federated Learning" (NeurIPS'20)
fl-pytorch
Federated Learning based on pytorch
federated-pytorch-test
Federated learning with PyTorch (federated averaging and consensus optimization): with 'reduced' bandwidth