Federated learning framework for mobile edge computing networks

Federated learning framework for mobile edge computing networks

The continuous growth of smart devices needing processing has led to moving storage and computation from cloud to the network edges, giving rise to the edge computing paradigm. Owing to the limited capacity of edge computing nodes, the presence of popular applications in the edge nodes results in si...

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Bibliographic Details
Main Authors: Romano Fantacci, Benedetta Picano
Format: Article
Language:English
Published: Wiley 2019-11-01
Series:CAAI Transactions on Intelligence Technology
Subjects:
data privacy
neural nets
learning (artificial intelligence)
virtual machines
mobile computing
computer networks
federated learning framework
mobile edge computing networks
smart devices
moving storage
network edges
edge computing paradigm
edge computing nodes
content requests
prediction demand
classical prediction approaches
personal users
central unit
learning procedures
multiple users
sensitive data
application demand prediction problem
popular application types
high accuracy levels
predicted applications demand
local training process
deep learning
Online Access:https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0049

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https://digital-library.theiet.org/content/journals/10.1049/trit.2019.0049

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