在这篇工作中,我们重点是在优化Non-IID、Unbalanced属性,以及通信约束这个关键本质。部署的联邦优化系统还必须解决无数实际问题:随着数据的增加和删减,客户端数据集也在变化;客户端可用性以复杂的方式与本地数据分布相关(如:来自美式英语使用者的电话会比来自英式英语使用者的电话更可能在不同时间插电(充电等));客户...
Simple implementation of FedAvg, a Federated Learning algorithm. federated-learningfedavg UpdatedOct 10, 2023 Python moonfederatedfederated-learningfedavgfedprox UpdatedJul 20, 2022 Jupyter Notebook PyTorch implementation of federated learning on MNIST ...
For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is ...
so that the latter phase could be totally covered by the former phase. Compared to vanilla FedAvg, Overlap-FedAvg was further developed with a hierarchical computing strategy, a data compensation mechanism, and a nesterov accelerated gradients (NAG) algorithm. In Particular, Overlap-FedAvg...
.搭建联邦学习系统,复现FedAvg算法。要求:a)参考论文“Communication-EfficientLearningofDeepNetworksfromDecentralizedData",根据其中的Algorithm1实现FederatedAveraging算法。各个客户机的训练用循环实现即可。b)推荐使用python+pytorch进行编程,其中python与pytorch推荐使用conda()进行安装。c)服务器端和客户端分别写到两个文件...
PFL training using cwFedAvg algorithm #cwFedAvg with WDRcdsystem python main.py -lbs 10 -nc 20 -jr 1 -nb 10 -data Cifar10 -m cnn -algo cwFedAvg -gr 1000 -cw -wdr -wd 10 -did 0 -go cnn#cwFedAvg without WDRcdsystem python main.py -lbs 10 -nc 20 -jr 1 -nb 10 -data Cifar...
In this paper, we show how the excess risks of personalized federated learning using a smooth, strongly convex loss depend on data heterogeneity from a minimax point of view, with a focus on the FedAvg algorithm (McMahan et al., 2017) and pure local training (i.e., clients solve ...
For strongly convex and convex problems, we also characterize the corresponding convergence rates for the Nesterov accelerated FedAvg algorithm, which are the first linear speedup guarantees for momentum variants of FedAvg in convex settings. Empirical studies of the algorithms in various ...
To the best of our knowledge, our approach is the first to combine a meta-based FedAvg algorithm with client-level differential privacy in federated learning. In summary, the main contributions of our paper are as follows: We propose CLDP-pFedAvg, a novel client-level differentially private ...
Our paper is a tentative theoretical understanding towardsFedAvgand how different sampling and averaging schemes affect its convergence. Our code is based on the codes forFedProx, another federated algorithm used in heterogeneous networks. Usage