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。...
Integration with learned database systems; Non-IID resistant sampling for partial participation; Privacy-preserving data mining; Query on Federated Databases; A Party with a Single Label:从表 3 可以看出,如果每一方只有单一标签的数据,FL 算法的准确性很差; Fast Training:从表 4 可以看出,现有的 FL ...
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FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving t... X Zhang,M Hong,S Dhople,... - arXiv 被引量...
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。
Federated Learning Algorithm (Pytorch) : FedAvg, FedProx, MOON, SCAFFOLD, FedDyn - meng1103/Federated-Learning-Non-IID
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 a single class of data. We further show that this ...
Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. However, the key challenge in federated learning is that the clients have significant statistical heterogeneity among their local data distributions, which would cause ...