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This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges
FOS: Computer and information sciences, Computer Science - Machine Learning, Distributed neural networks, Computer Science - Artificial Intelligence, Cyber-physical systems, Edge artificial intelligence, Ubiquitous and pervasive computing, cyber-physical systems; distributed neural networks; edge artificial intelligence; human-centred artificial intelligence; ubiquitous and pervasive computing, Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Human-centred artificial intelligence
FOS: Computer and information sciences, Computer Science - Machine Learning, Distributed neural networks, Computer Science - Artificial Intelligence, Cyber-physical systems, Edge artificial intelligence, Ubiquitous and pervasive computing, cyber-physical systems; distributed neural networks; edge artificial intelligence; human-centred artificial intelligence; ubiquitous and pervasive computing, Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Human-centred artificial intelligence
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 19 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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| downloads | 35 |

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