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,...
محفوظ في:
| المؤلف الرئيسي: | Alaa Awad Abdellatif (17151163) (author) |
|---|---|
| مؤلفون آخرون: | Naram Mhaisen (16870071) (author), Amr Mohamed (3508121) (author), Aiman Erbad (14150589) (author), Mohsen Guizani (12580291) (author), Zaher Dawy (17151166) (author), Wassim Nasreddine (9149936) (author) |
| منشور في: |
2022
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
Reputation-Aware Multi-Agent DRL for Secure Hierarchical Federated Learning in IoT
حسب: Noora Mohammed Al-Maslamani (17541798)
منشور في: (2023) -
Edge intelligence for network intrusion prevention in IoT ecosystem
حسب: Mansura Habiba (17808302)
منشور في: (2023) -
Edge intelligence for network intrusion prevention in IoT ecosystem
حسب: Mansura, Habiba
منشور في: (2023) -
Thing Artifact-based Design of IoT Ecosystems
حسب: Zakaria Maamar (17246311)
منشور في: (2023) -
Security Assessment of Low-Resource Edge Devices for IoT Systems
حسب: Shapsough, Shams Eddeen Yousef
منشور في: (2020)