Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

The performance of federated learning (FL) over wireless networks depend on the reliability
of the client-server connectivity and clients' local computation capabilities. In this article we …

In this work, we propose a novel joint client scheduling and resource block (RB) allocation
policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to …

Machine learning (ML) is a promising enabler for the fifth generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

Ultra-reliable communication (URC) is a key enabler for supporting immersive and mission-
critical 5G applications. Meeting the strict reliability requirements of these applications is …

Federated learning (FL) is a distributed paradigm for collaboratively learning models without
having clients disclose their private data. One natural and practically relevant metric to …

Shannon's source entropy formula is not appropriate to measure the uncertainty of non-
stationary processes. In this paper, we propose a new entropy measure for non-stationary …

It is well known that Max-Weight type scheduling algorithms are throughput optimal since
they achieve the maximum throughput while maintaining the network stability. However, the …

Most of the opportunistic scheduling algorithms in literature assume that full wireless
channel state information (CSI) is available for the scheduler. However, in practice obtaining …

Significant advances in edge and mobile computing capabilities enable machine learning
(ML) and artificial intelligence (AI) to occur at geographically diverse locations in networks …