PyTorch implementation of Federated Learning algorithms FedSGD, FedAvg, FedAvgM, FedIR, FedVC, FedProx and standard SGD, applied to visual classification. Client distributions are synthesized with arbitrary non-identicalness and imbalance (Dirichlet priors). Client systems can be arbitrarily heterogeneous....
return w_avg Net.py: Net.py定义了三个模型MLPCNNMnist以及 CNNCifar test.py:#该函数用于测试模型net_g在数据集合datatest上的表现,很简单的训练过程 (args后面介绍,用于接收命令行参数的) 基础不是很好的话可以看看这个PyTorch深度学习快速入门教程(绝对通俗易懂!)【小土堆】_哔哩哔哩_bilibili def test_img(...
Federated Learning Algorithm (Pytorch) : FedAvg, FedProx, MOON, SCAFFOLD, FedDyn - meng1103/Federated-Learning-Non-IID
All the codes we used to draw figures are inplot/. You can find some choices of hyperparameters in both our paper and the scripts inplot/. Dependency Pytorch = 1.0.0 numpy = 1.16.3 matplotlib = 3.0.0 tensorboardX
pytorch>=1.12.0 torchvision>=0.13.0 wandb>=0.12.19Conda Installationconda env create -f environment.yml How To UseMajor ArgumentsFlagOptionsDefaultInfo --data_root String "../datasets/" path to data directory --model_name String "cnn" name of the model (cnn, mlp) --non_iid Int (0 or...
torch - PyTorch 1.8.0 documentation torchvision - Torchvision master documentation README.md 文件记录了github代码贡献者对整个工程的详解。(文档作用) data 文件夹用来存放相应的数据,可以发现一开始里面是 mnist 和 cifar 的空文件夹,在待会儿代码讲解过程中会提到怎么加载数据。
联邦元学习算法Per-FedAvg的原理是什么? PyTorch实现联邦元学习算法Per-FedAvg有哪些关键步骤? 如何在PyTorch中优化Per-FedAvg算法的性能? I. 前言 Per-FedAvg的原理请见:arXiv | Per-FedAvg:一种联邦元学习方法。 II. 数据介绍 联邦学习中存在多个客户端,每个客户端都有自己的数据集,这个数据集他们是不愿意共享...
联邦学习(Federated Learning)结构由Server和若干Client组成,在联邦学习方法过程中,没有任何用户数据被传送到Server端,这保护了用户数据的隐私。此外,通信中传输的参数是特定于改进当前模型的,因此一旦应用了它们,Server就没有理由存储它们,这进一步提高了安全性。
The implementation of federated average learning[1] based on TensorFlow and PyTorch respectively. Some codes refers tohttps://github.com/Zing22/tf-fed-demo,https://tensorflow.googlesource.com/tensorflow/+/master/tensorflow/g3doc/tutorials/mnist/input_data.pyandhttps://github.com/persistforever/cifar...
PyTorch 1.0.0 TorchVision 0.2.1 pdsh, paralle shell All experiments in this paper are conducted on a private cluster with 16 machines connected via Ethernet, each of which is equipped with one NVIDIA TitanX GPU. We treat each machine as one client (worker) in the federated learning setting....