Client-Edge-Cloud Hierarchical Federated Learning联邦学习(云 -边-端)模型笔记 摘要 摘要--联合学习是一个协作式的机器学习框架,用于训练深度学习模型而不需要访问客户的私人数据。以前的工作假设在云端或边缘有一个中央参数服务器。云服务器可以访问更多的数据,但有过多的通信开销和较长的延迟,而边缘服务器享有与...
Client-edge-cloudPersonalized modelNon-independent and identically distributedMobile edge computing aims to deploy mobile applications at the edge of wireless networks. Federated learning in mobile edge computing is a forward-looking distributed framework for deploying deep learning algorithms in many ...
we present the first personalized federated learning algorithm based on the client-edge-cloud structure. The edge server is responsible for model personalization and employs a learnable mixing parameter to mix the local model and the global model. We also utilize two ...
Personalized client‑edge‑cloud hierarchical federated learning in mobile edge computing Chunmei Ma1, Xiangqian Li1, Baogui Huang1*, Guangshun Li1 and Fengyin Li1 Abstract Mobile edge computing aims to deploy mobile applications at the edge of wireless networks....
Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing 搁浅 来自专栏 · 边缘计算论文阅读 2 人赞同了该文章 移动边缘计算中能量平衡客户端选择的联邦学习 移动边缘计算(MEC)被认为是一种很有前途的技术,可以提供多种应用服务的无缝集成。在MEC的边缘客户端上进行联邦学习(FL),用于数据...
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge 摘要 我们设想了一个用于机器学习(ML)技术的移动边缘计算(MEC)框架,它利用分布式客户端数据和计算资源来训练高性能ML模型,同时保留客户端隐私。为了实现这一未来目标,本文旨在扩展联邦学习(FL)这个分散学习框架,使模型的隐私保护训练能...
Implementation of HierFAVG algorithm inClient-Edge-Cloud Hierarchical Federated Learningwith Pytorch. For running HierFAVG with mnist and lenet: python3 hierfavg --dataset mnist --model lenet --num_clients 50 --num_edges 5 --frac 1 --num_local_update 60 --num_edge_aggregation 1 --num...
(5) Clients, edge nodes, and cloud server begin the FL training process. The edge node obtains the clients participating in each training round through the client selection algorithm of the second stage according to its own candidate client set. (6) The selected clients train the global model...
Federated learning: Challenges, methods, and future directions IEEE Signal Process. Mag. (2020) NishioT. et al. Client selection for federated learning with heterogeneous resources in mobile edge AbdulRahmanS. et al. FedMCCS: Multicriteria client selection model for optimal IoT federated learning ...
The web link: GitHub - IBM/adaptive-federated-learning: Code for paper "Adaptive Federated Learning in Resource Constrained Edge Computing Systems"client.py import socket import time import …