论文地址:Federated Learning with Non-IID Data 一、 Introduction 介绍 这部分内容先是介绍了FL的由来和发展,简单介绍了Fedavg算法(不了解的小伙伴需要看一下2016年谷歌那篇论文,流程比较简单),说明了一下FL通信的问题和研究,最后引出了FL的Non-IID问题,在一些特定的Non-IID数据集上Fedavg是可以收敛的,但是其他情...
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 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 ...
23 p. Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning 18 p. Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use 5 p. Revisiting Joule-expansion experiments with a quantum gas 关于...
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。 数据异构性的影响 ...
Federated Learning with Non-IID Data IID:独立同分布 (idependently and identically distributed, IID) 论文链接 Abstract 联合学习使资源受限的边缘计算设备(例如移动电话和IoT设备)能够学习共享的预测模型,同时将训练数据保持在本地。这种去中心化的训练模型方法提供了隐私,安全性,监管和经济利益。在这项工作中,我们...
Non-IID Data Federated Learning with Non-IID Data IID: 独立同分布 (idependently and identically distributed, IID) 论文链接 Abstract 联合学习使资源受限的边缘计算设备(例如移动电话和IoT设备)能够学习共享的预测模型,同时将训练数据保持在本地。这种去中心化的训练模型方法提供了隐私,安全性,监管和经济利益。
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL)is becoming popular in Int... T Liu,J Ding,T Wang,... 被引量: 0发...
Federated Learning Algorithm (Pytorch) : FedAvg, FedProx, MOON, SCAFFOLD, FedDyn - meng1103/Federated-Learning-Non-IID