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Distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and Research Directions
Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resources.
Learning-driven Zero Trust in Distributed Computing Continuum Systems
To overcome such challenges, we present a novel learning-driven ZT conceptual architecture designed for DCCS.
Equilibrium in the Computing Continuum through Active Inference
We present our framework for collaborative edge intelligence enabling individual edge devices to (1) develop a causal understanding of how to enforce their SLOs, and (2) transfer knowledge to speed up the onboarding of heterogeneous devices.
Active Inference on the Edge: A Design Study
Machine Learning (ML) is a common tool to interpret and predict the behavior of distributed computing systems, e. g., to optimize the task distribution between devices.
Designing Reconfigurable Intelligent Systems with Markov Blankets
Compute Continuum (CC) systems comprise a vast number of devices distributed over computational tiers.
