The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

Federated learning (FL) is an emerging distributed machine learning method that empowers
in-situ model training on decentralized edge devices. However, multiple simultaneous FL …

The proliferation of machine learning (ML) applications has lately witnessed a considerable
shift to more distributed settings, even reaching hand-held mobile devices; there, contrary to …

Deep Neural Networks (DNNs) have had a significant impact on domains like autonomous
vehicles and smart cities through low-latency inferencing on edge computing devices close …

Traffic Sign Detection (TSD) is a complex and fundamental task for developing autonomous
vehicles; it is one of the most critical visual perception problems since failing in this task may …

Edge intelligence leverages computing resources on the network edge to provide artificial
intelligence (AI) services close to network users. As it enables fast inference and distributed …

Deep learning (DL) has been widely applied to many domains. Unique challenges in
engineering DL systems are posed by the programming paradigm shift from traditional …

Federated learning (FL) enables multiple devices to collaboratively train a shared model
while preserving data privacy. However, despite its emerging applications in many areas …

Pest recognition technology has rapidly progressed in a short period with the development
of deep convolutional neural networks. However, the vast calculation burden of these …

We develop a new Lagrange coded blockchain model for Internet-of-Things (IoT) systems
based on Lagrange coded computing (LCC). In the model, a mining task assigned to a …