Edge Computing is widely recognized as a key technology, supporting innovative services
for a wide ecosystem of companies, ranging from operators, infrastructure owners …

Large-scale IoT applications based on machine learning (ML) demand both edge and cloud
processing for, respectively, AI inference and ML training tasks. Context-aware applications …

Given its advantages in low latency, fast response, context-aware services, mobility, and
privacy preservation, edge computing has emerged as the key support for intelligent …

Abstract In recent years, Artificial Intelligence (AI) inference at the network edge has gained
substantial research attention. This interest is driven by the increasing number of use cases …

The Internet of Things (IoT) has enabled the development of the real-time edge computing
institute for distributed cloud networks. This technology makes it possible for devices …

The burgeoning growth of the Internet of Things (IoT) has prompted a paradigm shift in
computing architectures, leading to the emergence and rapid evolution of edge computing …

Don't Miss the Train: A Case for Systems Research into Training on the Edge Page
1 Don’t Miss the Train: A Case for Systems Research into Training on the Edge Prashanthi …

In the last decade, deep learning has rapidly infiltrated the consumer end, mainly thanks to
hardware acceleration across devices. However, as we look toward the future, it is evident …

TECoSA–a university‐based research center in collaboration with industry–was established
early in 2020, focusing on Trustworthy Edge Computing Systems and Applications. This …

Examines the new challenges posed by human-driven edge computing. The massive
proliferation of personal computing devices is opening new human-centered designs that …