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Federated Learning in NOMA Networks: Convergence, Energy and Fairness-Based Design
(
IEEE
, 2022 , Conference Paper)
Federated Learning (FL) is a collaborative machine learning (ML) approach, where different nodes in a network contribute to learning the model parameters. In addition, FL provides several attractive features such as data ...
Federated Learning for UAV Swarms under Class Imbalance and Power Consumption Constraints
(
IEEE
, 2021 , Conference Paper)
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches. Despite the abundance of such advantages, ...
Secure Medical Data Sharing for Healthcare System
(
IEEE
, 2022 , Conference Paper)
A new generation of advanced information technologies are used nowadays by healthcare systems to provide access to affordable and high-quality healthcare services. However, such services, generally require a large amount ...
Cooperative Machine Learning Techniques for Cloud Intrusion Detection
(
IEEE
, 2021 , Conference Paper)
Cloud computing is attracting a lot of attention in the past few years. Although, even with its wide acceptance, cloud security is still one of the most essential concerns of cloud computing. Many systems have been proposed ...
Data Augmentation for Intrusion Detection and Classification in Cloud Networks
(
IEEE
, 2021 , Conference Paper)
Cloud computing is a paradigm that provides multiple services over the internet with high flexibility in a cost-effective way. However, the growth of cloud-based services comes with major security issues. Recently, machine ...
Machine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes
(
IEEE
, 2021 , Conference Paper)
COVID-19 is a virus that has infected more than one hundred and fifty million people and caused more than three million deaths by 13th of Mai 2021 and is having a catastrophic effect on the world population's safety. ...