Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution...
Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM 1. Introduction Due to growing demand for energy, renewable energy sources have received increasing attention in recent years. Photovoltaic (PV) energy is one of the most promising solutions for renewable energy. According to the...
Semi-supervised learningMulti-branch modelIn the vanilla federated learning (FL) framework, the central server distributes a globally unified model to each client and uses labeled samples for training. However, in most cases, clients are equipped with different devices and are exposed to a variety...
SemiFL Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training NIPS 2022 momo 闪击与噬咬,胜利不是来自一击穿心,而是千刀万剐 创作声明:内容包含虚构创作 3 人赞同了该文章 1 Introduction 本文提出SemiFL解决直接将SSL与FL结合非常困难的问题,SemiFL让本地客户端用完全无标签的图片进行多...
In order to solve the problem of device heterogeneity in federated learning, FedAsync proposes asynchronous federated learning, that is, the server and the client interact in an asynchronous manner, that is, the server updates the global model immediately after receiving the local model. Communication...
Federated Learning (FL) has gained prominence as a method for intrusion detection in Internet of Things (ID-IoT) edge devices, with a central server, aiming to address privacy concerns. However, FL-based intrusion detection faces challenges, including privacy risks from transmitting model parameters...
GraphFL 框架受最近的元学习方法启发,称为 model-agnostic meta-learning (MAML),MAML 对新任务具有快速适应能力。给定一组从基础分布中抽取的任务,MAML 学习一个与任务无关的初始化,在几步梯度更新后在所有任务上都表现良好。MAML 适合与 FL 进行结合。可以把每个任务看作是一个客户端,而与任务无关的初始化是...
semi-supervised learning literature survey:半监督学习文献综述 热度: 大语言模型综述 A Survey of Large Language Models 热度: 目标分类和目标检测综述(2D和3D数据) A survey of Object Classification and Detection based on 2D_3D data 热度: ASurveytowardsFederatedSemi-supervisedLearning ...
Federated Learning (FL) is an emerging distributed machine learning framework that allows edge devices to collaborative train a shared global model without transmitting their sensitive data to centralized servers. However, it is extremely challenging to apply FL in practical scenarios because the statistic...
切换模式 登录/注册 陆加柒等于十三 UCAS|Statistics 这俩有点像 | federated learning=block-wise missing? 这两个setting似乎是一回事,只是后者一般还玩一点semi-supervised的东西 发布于 2024-06-27 15:36・IP 属地北京 分享 收藏 ...