Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation
paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless …

Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Federated learning (FL) is a rapidly growing research field in machine learning. However,
existing FL libraries cannot adequately support diverse algorithmic development; …

Recently, federated learning (FL) techniques have enabled multiple users to train machine
learning models collaboratively without data sharing. However, existing FL algorithms suffer …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Decentralised learning is attracting more and more interest because it embodies the
principles of data minimisation and focused data collection, while favouring the transparency …

In edge computing (EC), federated learning (FL) enables massive devices to collaboratively
train AI models without exposing local data. In order to avoid the possible bottleneck of the …

Unmanned aerial vehicles (UAVs), or drones, are envisioned to support extensive
applications in next-generation wireless networks in both civil and military fields …

Intrusion detection systems are evolving into intelligent systems that perform data analysis
searching for anomalies in their environment. The development of deep learning …

The rapid growth of memory and computation requirements of large language models
(LLMs) has outpaced the development of hardware, hindering people who lack large-scale …