Edge computing promises to decentralize cloud applications while providing more bandwidth and reducing latency. These promises are delivered by moving application-specific computations between the cloud, the data-producing devices, and the network infrastructure components at the edges of wireless and fixed networks. Meanwhile, the current Artificial Intelligence (AI) and Machine Learning (ML) methods assume computations are conducted in a powerful computational infrastructure, such as datacenters with ample computing and data storage resources available. In this Dagstuhl Seminar, we address challenges that include 1) large-scale deployment of the edge-cloud continuum, 2) energy optimization and sustainability of such large-scale AI/ML learning and modelling, and 3) trustworthiness, security, and ethical questions related to the intelligent edge-cloud continuum.

The output of this Dagstuhl Seminar will include a roadmap for a large-scale, energy-efficient, and safety- and privacy-aware Edge Intelligence. We welcome participants for discussion and give a 10-15min talk on their own perspectives on the following topics:

1. The success of the edge-cloud continuum depends on the deployment of edge and AI-driven services as well as software-hardware DevOps. This includes novel directions on new programming paradigms, system architecture, and runtime frameworks for achieving large-scale deployment.
2. For future intelligent embedded infrastructures (e.g., roadside units, micro base stations), it is necessary to sustainably manage the pipeline of data acquisition, transfer, computation, and storage. This includes exploration of a tradeoff between accuracy and energy consumption, applications that would be satisfied with an ‘acceptable’ accuracy instead of exact and absolutely correct results, and other new dimensions of accuracy optimization design.
3. Due to the distributed deployment with deep insights into a personal context, the safety and perceived trustworthiness of Edge Intelligence services shall be investigated through the lens of multiple stakeholders (e.g., end-users, public sectors, ISP). This includes critical building blocks that can ensure transparency and explainability, especially in the training and deployment of Edge Intelligence in decentralized, uncontrolled environments.
4. The interplay of Edge and AI/ML is the fourth topic to be explored in our seminar given the functional and non-functional concerns such as safety, privacy, and ethical issues.

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Eyal De Lara, Aaron Ding, Schahram Dustdar, and Ella Peltonen