We envision a human-AI ecosystem in which AI-enabled devices act as proxies of humans and try to learn collectively a model in a decentralized way. Each device will learn a local model that needs to be combined with the models learned by the other nodes, in order to improve both the local and global knowledge. The challenge of doing so in a fully-decentralized AI system entails understanding how to compose models coming from heterogeneous sources and, in case of potentially untrustworthy nodes, decide who can be trusted and why. In this micro-project, we focus on the specific scenario of model “gossiping” for accomplishing a decentralized learning task and we study what models emerge from the combination of local models, where combination takes into account the social relationships between the humans associated with the AI. We will use synthetic graphs to represent social relationships, and large-scale simulation for performance evaluation.


Paper (most likely at conference/workshop, possibly journal)

Simulator (fallback plan if a paper cannot be produced at the end of the micro-project)

Project Partners:

  • CNR-IIT, Andrea Passarella
  • CNR, Andrea Passarella
  • CEU, Gerardo Iniguez

Primary Contact: Andrea Passarella, CNR-IIT