Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …

Artificial Intelligence (AI) is undoubtedly one of the most actively debated technologies,
providing auspicious opportunities to contribute to individuals' well-being, the success and …

Selecting a final machine learning (ML) model typically occurs after a process of
hyperparameter optimization in which many candidate models with varying structural …

An organization's ability to develop Machine Learning (ML) applications depends on its
available resource base. Without awareness and understanding of all relevant resources as …

Machine Learning can be used with cybersecurity data to protect organizations by using
artificial intelligence (AI) to generate rules and models for thread detection. Cloud platforms …

Since the complexity of large-scale and scientific computation, workflow has been used for
task decomposition in cloud computing. A dynamic resource allocation algorithm based on …

Nowadays, the dependency on high-performance digital mobile connectivity is not limited to
human usage but also the intelligent objects increasingly deployed to serve the needs of …

Federated Learning (FL) is a distributed Machine Learning (ML) technique that can benefit
from cloud environments while preserving data privacy. We propose Multi-FedLS, a …

High computational demands of complex deep learning models led to workload distribution
across multiple machines. Many frameworks for distributed machine learning (DML) have …

Cloud computing has become popular for training deep‐learning (DL) models, avoiding the
costs of acquiring and maintaining on‐premise systems. SageMaker is a cloud service that …