Special Session at IEEE MLSP 2025 in Instambul, Turkey

ABOUT
The increasing pervasiveness of edge devices (as the Internet of Things) for AI-based services (e.g., healthcare, structural health monitoring, environmental monitoring, wireless communication) and the high availability, velocity, and volatility of data generated and collected at the edge of the Internet are pushing toward a paradigm shift in the design of AI-based systems. Machine Learning (ML) and data processing are moving from powerful and remote data centers to distributed systems at the edge, working in proximity to where data are physically generated and/or collected. Differently from cloud data centers, distributed edge solutions are often deployed in resource-constrained devices, which limits the direct implementation of current state-of-the-art, energy-intensive ML models.