[go: up one dir, main page]

Chen et al., 2022 - Google Patents

Device scheduling for wireless federated learning with latency and representativity

Chen et al., 2022

Document ID
3694260817604317564
Author
Chen Z
Yi W
Deng Y
Nallanathan A
Publication year
Publication venue
2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

External Links

Snippet

Existing device scheduling methods in wireless fed-erated learning (FL) mainly focused on selecting the devices with maximum gradient norm or loss function and requires all devices to perform local training in each round. This may produce extra training costs and schedule …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W72/00—Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
    • H04W72/04—Wireless resource allocation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0058—Allocation criteria
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/16—Arrangements for providing special services to substations contains provisionally no documents
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATIONS NETWORKS
    • H04W52/00—Power Management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas
    • H04B7/022—Site diversity; Macro-diversity
    • H04B7/024—Co-operative use of antennas of several sites, e.g. in co-ordinated multipoint or co-operative multiple-input multiple-output [MIMO] systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna systems, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received

Similar Documents

Publication Publication Date Title
Ren et al. Scheduling for cellular federated edge learning with importance and channel awareness
Sun et al. Energy-aware analog aggregation for federated learning with redundant data
Lan et al. Progressive feature transmission for split classification at the wireless edge
Zhang et al. Communication-efficient federated edge learning via optimal probabilistic device scheduling
Balakrishnan et al. Resource management and fairness for federated learning over wireless edge networks
Su et al. Data and channel-adaptive sensor scheduling for federated edge learning via over-the-air gradient aggregation
Chu et al. Federated learning over wireless channels: Dynamic resource allocation and task scheduling
Chen et al. Exploring representativity in device scheduling for wireless federated learning
Chen et al. Robust federated learning for unreliable and resource-limited wireless networks
Peng et al. How to tame mobility in federated learning over mobile networks?
Michelusi et al. Cross-layer estimation and control for cognitive radio: Exploiting sparse network dynamics
Chen et al. Device scheduling for wireless federated learning with latency and representativity
Yin et al. Joint user scheduling and resource allocation for federated learning over wireless networks
Lin et al. CFLIT: Coexisting federated learning and information transfer
Banerjee et al. Game theoretic analysis of an adversarial status updating system
Fan et al. Mobility-aware joint user scheduling and resource allocation for low latency federated learning
Yu et al. Dynamic transmit covariance design in MIMO fading systems with unknown channel distributions and inaccurate channel state information
Gao et al. Multi-tier client selection for mobile federated learning networks
Zhang et al. Federated multi-task learning with non-stationary heterogeneous data
Merluzzi et al. Energy-efficient classification at the wireless edge with reliability guarantees
Yang et al. Q-learning for content placement in wireless cooperative caching
Pei et al. Minimizing age of information in UAV-assisted edge computing system with multiple transmission modes
US11411600B2 (en) Processing of uplink data streams
Chen et al. Communication-efficient federated learning with heterogeneous devices
Qi et al. Two-dimensional compressive spectrum sensing in collaborative cognitive radio networks