Knowledge sharing and preference harmonisation across a hub-spoke structure for data-sparse settings

Contact person: Geir Kjetil Sandve

Status: Project

Imagine you are deploying AI in a low-resource area in Ethiopia to forecast climate-sensitive diseases like malaria. You have almost no local data, but a massive global model exists trained with lots of data from other places. Right now, you face a forced choice: either build a standalone local model that probably fails due to data scarcity, or accept a global model that forces generic assumptions on you. The real problem is that the global model doesn't care about your local context – for example it uses its own loss function. It doesn't know that for your specific task, you need another loss function.

Our project solves this fundamental tension between global learning and local specificity and autonomy. We propose a Hub-and-Spoke framework designed specifically for settings where spokes cannot simply take the hub model and use it. In our architecture, the Hub holds shared data, computational power, and common AI models. The Spokes represent local contexts, particular users or customers, even individual patients. The Spoke wants to solve the same task as the hub, but in its own way. Importantly, the spoke has an own private loss function that it would have liked the hub to use in training, and that it want the spoke-based model to follow. The core innovation of our work is Spoke Autonomy. Methodologically, this project links to world models, alignment steering, knowledge representation, agentic AI.

So this project bridges the gap between centralized AI power and local decision-making. It ensures that when AI is deployed in high-impact, data-sparse environments, stakeholders never have to sacrifice their local autonomy just to benefit from global tools.

So what real-world problem will we be able to solve in a new and very competitive way? If you have a situation where you have many users and you want to tell them that with your model, service or product, they really can personalise the model, adapt it to their needs and priorities. If you make autonomy and freedom of your clients/user your marketing strength. Talk to us, we need your problem as case!