[go: up one dir, main page]

Nishio et al., 2019 - Google Patents

Client selection for federated learning with heterogeneous resources in mobile edge

Nishio et al., 2019

View PDF
Document ID
8447543133410898984
Author
Nishio T
Yonetani R
Publication year
Publication venue
ICC 2019-2019 IEEE international conference on communications (ICC)

External Links

Snippet

We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal, this …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W72/00Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/04Wireless resource allocation
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W84/00Network topologies
    • H04W84/18Self-organizing networks, e.g. ad-hoc networks or sensor networks
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network-specific arrangements or communication protocols supporting networked applications
    • H04L67/10Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W28/00Network traffic or resource management
    • H04W28/02Traffic management, e.g. flow control or congestion control
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATIONS NETWORKS
    • H04W4/00Mobile application services or facilities specially adapted for wireless communication networks
    • H04W4/06Selective distribution or broadcast application services; Mobile application services to user groups; One-way selective calling services
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run

Similar Documents

Publication Publication Date Title
Nishio et al. Client selection for federated learning with heterogeneous resources in mobile edge
Feng et al. Computation offloading in mobile edge computing networks: A survey
Qu et al. Context-aware online client selection for hierarchical federated learning
Yoshida et al. MAB-based client selection for federated learning with uncertain resources in mobile networks
Jiang Cellular traffic prediction with machine learning: A survey
Fadlullah et al. HCP: Heterogeneous computing platform for federated learning based collaborative content caching towards 6G networks
Zhou et al. Resource sharing and task offloading in IoT fog computing: A contract-learning approach
Liu et al. Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks
Deng et al. Task allocation algorithm and optimization model on edge collaboration
CN111445111B (en) A task allocation method for power Internet of things based on edge collaboration
EP3742669B1 (en) Machine learning in radio access networks
Nomikos et al. A survey on reinforcement learning-aided caching in heterogeneous mobile edge networks
Fu et al. Traffic prediction-enabled energy-efficient dynamic computing resource allocation in CRAN based on deep learning
Hoang et al. Deep reinforcement learning-based online resource management for UAV-assisted edge computing with dual connectivity
CN107708152B (en) A Task Offloading Method for Heterogeneous Cellular Networks
CN113490184A (en) Smart factory-oriented random access resource optimization method and device
Zheng et al. MEC-enabled wireless VR video service: A learning-based mixed strategy for energy-latency tradeoff
Lin et al. Deep reinforcement learning-based task scheduling and resource allocation for NOMA-MEC in Industrial Internet of Things
Li et al. Deep reinforcement learning-based mining task offloading scheme for intelligent connected vehicles in UAV-aided MEC
Sun et al. A resource allocation scheme for edge computing network in smart city based on attention mechanism
Sun et al. A DQN-based cache strategy for mobile edge networks
Kumar et al. Quality of service‐aware adaptive radio resource management based on deep federated Q‐learning for multi‐access edge computing in beyond 5G cloud‐radio access network
Younis et al. Energy-latency computation offloading and approximate computing in mobile-edge computing networks
Chen et al. Towards dynamic resource allocation and client scheduling in hierarchical federated learning: A two-phase deep reinforcement learning approach
CN113747450B (en) A service deployment method, device and electronic equipment in a mobile network