RL-PDNN: Reinforcement Learning for Privacy-Aware Distributed Neural Networks in IoT Systems

RL-PDNN: Reinforcement Learning for Privacy-Aware Distributed Neural Networks in IoT Systems

<p>Due to their high computational and memory demand, deep learning applications are mainly restricted to high-performance units, e.g., cloud and edge servers. Particularly, in Internet of Things (IoT) systems, the data acquired by pervasive devices is sent to the computing servers for classif...

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Bibliographic Details
Main Author: Emna Baccour (16896366) (author)
Other Authors: Aiman Erbad (14150589) (author), Amr Mohamed (3508121) (author), Mounir Hamdi (14150652) (author), Mohsen Guizani (12580291) (author)
Published: 2021
Subjects:
Information and computing sciences
Artificial intelligence
Data management and data science
Distributed computing and systems software
Machine learning
Task analysis
Servers
Deep learning
Dynamic scheduling
Real-time systems
Performance evaluation
Biological neural networks
IoT devices
Distributed DNN
Privacy
White-box
Resource constraints
DQN
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