Over the past six decades, the computing systems field has experienced significant
transformations, profoundly impacting society with transformational developments, such as …

Abstract The Artificial Intelligence of Things (AIoT), a combination of the Internet of Things
(IoT) and Artificial Intelligence (AI), plays an increasingly important role in smart agriculture …

Federated learning (FL) is a technique for distributed machine learning that enables the use
of siloed and distributed data. With FL, individual machine learning models are trained …

The increasing adoption of edge computing in rural areas is leading to a substantial rise in
data generation, necessitating the need for development of advanced load balancing …

Serverless computing enables greater flexibility and efficiency in the cloud-to-edge
continuum. Artificial Intelligence and Machine Learning (AI/ML) applications benefit greatly …

Machine Learning (ML) is increasingly exploited in a wide range of application areas to
analyze data streams from large-scale sensor networks, train predictive models and perform …

In this paper we report our experiences from the migration of an AI model inference process,
used in the context of an E-health platform to the Function as a Service model. To that …

Federated Learning (FL) is a recent approach for distributed Machine Learning (ML) where
data are never communicated to a central node. Instead, an ML model (for example, a deep …

Abstract The Internet of Things (IoT) and Edge-Cloud Computing have been trending
technologies over the past few years. In this work, we introduce the Enhanced Optimized …

Federated Learning (FL) is a decentralized machine learning paradigm where models are
trained on distributed devices and are aggregated at a central server. Existing FL …