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...
Agent 是一个位于联邦学习系统之上的智能决策层,它通过观察当前系统的状态(例如设备状态、网络条件等)来选择合适的客户端设备参与下一轮联邦训练。 它并不是某个具体的客户端设备,而是一个全局管理者,负责在系统级别优化联邦学习的资源分配和性能。 与客户端的关系: Agent 的行动(Action)是选择客户端设备参与训练。...
In this work, we focus on the statistical challenge of federated learning when local data is non-IID. We first show that the accuracy of federated learning reduces significantly, by up to 55% for neural networks trained for highly skewed non-IID data, where each client device trains only on...
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In this work, we focus on the statistical challenge of federated learning when local data is non-IID. We first show that the accuracy of federated learning reduces significantly, by up to 55% for neural networks trained for highly skewed non-IID data, where each client device trains only on...
Federated Learning Algorithm (Pytorch) : FedAvg, FedProx, MOON, SCAFFOLD, FedDyn - meng1103/Federated-Learning-Non-IID
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...
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...
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction 来自 arXiv.org 喜欢 0 阅读量: 698 作者:L Gao,H Fu,L Li,Y Chen,M Xu,CZ Xu 摘要: Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing ...