Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data

Communication-efficient hierarchical federated learning for IoT heterogeneous systems with imbalanced data

<p dir="ltr">Federated Learning (FL) is a distributed learning methodology that allows multiple nodes to cooperatively train a deep learning model, without the need to share their local data. It is a promising solution for telemonitoring systems that demand intensive data collection,...

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Bibliographic Details
Main Author: Alaa Awad Abdellatif (17151163) (author)
Other Authors: Naram Mhaisen (16870071) (author), Amr Mohamed (3508121) (author), Aiman Erbad (14150589) (author), Mohsen Guizani (12580291) (author), Zaher Dawy (17151166) (author), Wassim Nasreddine (9149936) (author)
Published: 2022
Subjects:
Information and computing sciences
Distributed computing and systems software
Machine learning
Distributed deep learning
Edge computing
Non-IID data
Internet of Things (IoT)
Intelligent health systems
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