For the last two decades the usual approach for IoT applications has been to leverage on cloud infrastructures to address the computational and storage limitations and constraints at end and edge nodes. However, offloading processing capabilities to the cloud requires transferring data from edge devices to a backend cloud infrastructure, dealing with the limitations imposed by the underlying communication channels, depending on the availability constraints of both those communication channels and the cloud infrastructure, establishing proper mechanisms to secure data in transit and data at rest at the backend, dealing with the costs incurred from the transmission of data and by the usage of the backend cloud infrastructure, etc.
The advent of end and edge nodes with increased computational and storage capabilities makes it possible to perform a large range of computations, with special focus on AI-based tasks on privacy-sensitive data, locally at the edge. This makes it possible to only rely on a backend cloud for communicating results of non-private data, for example by aggregation, or performing computations requiring the combination and further processing of results from different edge devices or historical data sources as it is done in federated learning approaches. In addition, this increase in computation capabilities also enables neighboring edge devices to perform tasks collaboratively, leveraging on each other's available computational resources, before offloading data and computations to a cloud backend.
This workshop will focus on AI and ML techniques related to edge computing systems, and security and privacy approaches in view of data sharing in order to enable the smart and sustainable planning and operation of resource constrained IoT and edge computing applications. The workshop is organized within the scope of the European ITEA3 MIRAI R&D project. It welcomes innovative contributions, early results and position papers addressing one or more of to the topics listed below and intends to foster informal discussions and cross-fertilization on the convergence of AI and edge computing.