Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra.Federated Learning with Non-IID Data arXiv:1806.00582. Paper TL;DR: Previous federated optization algorithms (such as FedAvg and FedProx) converge to stationary points of a mismatched objective function due to heterogeneity...
In Differentially Private Federated Learning (DPFL), gradient clipping and random noise addition disproportionately affect statistically heterogeneous data. As a consequence, DPFL has a disparate impact: the accuracy of models trained with DPFL tends to decrease more on these data. If the accuracy ...
相关代码已挂到github上 GitHub - Xtra-Computing/NIID-Bench: Federated Learning on Non-IID Data Silos: An Experimental Study (ICDE 2022)github.com/Xtra-Computing/NIID-Bench 背景介绍: 简单说,每一方利用本地数据训练一个结构相同的模型,将权重信息送至中心服务器进行聚合。将聚合后的权重返还至参与方...
Federated Learning on Non-IID Data with Local-drift Decoupling and Correction Code for paper - [Federated Learning on Non-IID Data with Local-drift Decoupling and Correction]We provide code to run FedDC, FedAvg, FedDyn, Scaffold, and FedProx methods....
code: https://github.com/yjlee22/FedShare 编辑:古月 Non_IID造成的精确度降低的原因归于权重散度。 文章证明了训练的权重散度的差异值是由每个设备的类的分布和全局分布之间的EMD距离限定的。这个上界由学习率,本地更新步长,和梯度值共同决定。文章提出一种数据共享策略改善non-IID的平衡。
Federated learning, Non-IID, Long-tailed learning, Distillation 1. Introduction 1.1 背景 近年来,越来越多的深度学习技术被部署在移动设备中,以处理来自不同来源的数据,例如相机、麦克风、GPS 和其他传感器。 这些数据在生成强大的预测模型以向用户提供更好的服务方面发挥着关键作用。但这过程中的数据传输对于服务...
The poorest model generalization performances are shown on institutions 2, 3 and 6, which have the smallest data contributions of the group. Collaborative learning is superior We evaluate the benefits of collaborative learning with respect to improving both scores on an institution’s own data, and...
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this ...
,wenkehuang,yemang}@whu.edu.cn https://github.com/xiyuanyang45/FedAS Abstract Personalized Federated Learning (PFL) is primarily de- signed to provide customized models for each client to bet- ter fit the non-iid distributed client data, which is a inherent challenge ...
Federated Learning on Non-IID Data with Local-drift Decoupling and Correction Code for paper - [Federated Learning on Non-IID Data with Local-drift Decoupling and Correction]We provide code to run FedDC, FedAvg, FedDyn, Scaffold, and FedProx methods....