Future communication networks will require increased flexibility, scalability, and data
computation capabilities to adequately respond to the growing number of service demands …

Federated learning (FL) is an emerging technology for empowering various applications that
generate large amounts of data in intelligent cyber-physical systems (ICPS). Though FL can …

The Benders' decomposition algorithm is a technique in mathematical programming for
complex mixed-integer linear programming (MILP) problems with a particular block …

In the technologically changing world, the demand for ultra‐reliable, faster, low power, and
secure communication has significantly risen in recent years. Researchers have shown …

To enable an in-depth study of power system operation and planning, the assessment of
standard reliability indices is inevitable. The Monte Carlo Simulation (MCS) approach is a …

The flourishing of the data era has led to a higher demand for hyper-scale data centers,
resulting in a great energy gap. It is necessary to comprehensively analyze the energy …

Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been
a subject of continuous attention. With the gradual maturation of QC platforms, it has …

Benders' decomposition (BD) algorithm constitutes a powerful mathematical programming
method of solving mixed-integer linear programming (MILP) problems with a specific block …

Federated learning (FL) is a distributed machine learning paradigm with a promising future,
which can preserve data privacy while training the global model collaboratively. However …

Embedding Service Function Chain (SFC) into the massive and resource-hungry Internet of
Things (IoT) substrate graph is a critical optimization research problem. Unfortunately, the …