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...
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。...
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 ...
Knowledge-Aware Federated Active Learning with Non-IID DataAbstract联合学习使多个分散的客户端能够在不共享本地训练数据的情况下进行协作学习。然而,在本地客户端上获取数据标签的昂贵注释成本仍然是利用本…
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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...
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。
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...
Federated Learning Algorithm (Pytorch) : FedAvg, FedProx, MOON, SCAFFOLD, FedDyn - meng1103/Federated-Learning-Non-IID