Byzantine machine learning: A primer
The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …
learning, consists of designing distributed algorithms that can train an accurate model …
Machine learning approaches for combating distributed denial of service attacks in modern networking environments
A Aljuhani - IEEE Access, 2021 - ieeexplore.ieee.org
A distributed denial of service (DDoS) attack represents a major threat to service providers.
More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by …
More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by …
QoS-ledger: Smart contracts and metaheuristic for secure quality-of-service and cost-efficient scheduling of medical-data processing
Quality-of-service (QoS) is the term used to evaluate the overall performance of a service. In
healthcare applications, efficient computation of QoS is one of the mandatory requirements …
healthcare applications, efficient computation of QoS is one of the mandatory requirements …
A systematic survey on fault-tolerant solutions for distributed data analytics: Taxonomy, comparison, and future directions
S Isukapalli, SN Srirama - Computer Science Review, 2024 - Elsevier
Fault tolerance is becoming increasingly important for upcoming exascale systems,
supporting distributed data processing, due to the expected decrease in the Mean Time …
supporting distributed data processing, due to the expected decrease in the Mean Time …
A federated reinforcement learning approach for optimizing wireless communication in UAV-enabled IoT network with dense deployments
F Yang, Z Zhao, J Huang, P Liu, A Tolba… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
In unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) networks, the
communication ranges between densely deployed IoT devices overlap, resulting in wireless …
communication ranges between densely deployed IoT devices overlap, resulting in wireless …
A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics
CA Marino, M Marufuzzaman - Computers & Industrial Engineering, 2020 - Elsevier
A Microgrid (MG) is a promising distributed technology to solve todays energy challenges.
They are changing how electricity is produced, transmitted, and distributed, enabling to …
They are changing how electricity is produced, transmitted, and distributed, enabling to …
A novel bearing fault diagnosis method using spark-based parallel ACO-K-means clustering algorithm
L Wan, G Zhang, H Li, C Li - IEEE Access, 2021 - ieeexplore.ieee.org
K-Means clustering algorithm is a typical unsupervised learning method, and it has been
widely used in the field of fault diagnosis. However, the traditional serial K-Means clustering …
widely used in the field of fault diagnosis. However, the traditional serial K-Means clustering …
An efficient parallel secure machine learning framework on GPUs
Machine learning is widely used in our daily lives. Large amounts of data have been
continuously produced and transmitted to the cloud for model training and data processing …
continuously produced and transmitted to the cloud for model training and data processing …
DADEM: Distributed attack detection model based on big data analytics for the enhancement of the security of internet of things (IoT)
HI Ahmed, AA Nasr, SM Abdel-Mageid… - International Journal of …, 2021 - igi-global.com
Abstract Nowadays, Internet of Things (IoT) is considered as part our lives and it includes
different aspects-from wearable devices to smart devices used in military applications. IoT …
different aspects-from wearable devices to smart devices used in military applications. IoT …
Apache Spark based kernelized fuzzy clustering framework for single nucleotide polymorphism sequence analysis
This paper introduces a kernel based fuzzy clustering approach to deal with the non-linear
separable problems by applying kernel Radial Basis Functions (RBF) which maps the input …
separable problems by applying kernel Radial Basis Functions (RBF) which maps the input …