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Decentralized AI

Decentralized AI is one AI Sweden’s strategic programs. While centralized AI systems with access to all data and information in the cloud or in a single device are easier to engineer and implement, decentralised systems are becoming increasingly important not least due to data privacy restrictions and limited bandwidth. Decentralized AI will play a critical role in the use of AI in society and ensure for example that hospitals or autonomous cars are able to share and benefit from knowledge centrally, while keeping the sensitive data safe and local. 

The program consists of developing an open lab environment, high-impact use cases, and solutions to core technical issues and legal challenges, while encouraging robust thought leadership in the field and boosting joint research projects together with partners. This will accelerate Sweden’s application of AI across sectors in a secure and efficient way.

Join us in accelerating the development of decentralized AI!

Edge Lab

Edge Lab

Edge Lab gives AI Sweden’s partners the chance to position themselves at the forefront of Edge AI and federated learning by scoping projects together and quickly build a working environment for experiments. 

Edge Lab

What is decentralized AI?

Decentralized AI refers to moving intelligence and learning out to different devices and organizations, training machine learning models on locally available (i.e. decentralized) data and making local decisions. It is a prerequisite for being able to apply AI on a broad scale in Sweden. The approach enables combining knowledge from several local datasets, without distributing the actual raw data between devices, locations, and organizations. In contrast to training algorithms on a centralized dataset, decentralized learning distributes models rather than the data itself.

Decentralized and federated learning approaches are quickly becoming one of the most
important areas of applied AI, mainly due to efficiency and privacy reasons. In real world
scenarios, data is often impossible to centralize in one single data center. As a result, if we want to make the most of all data generated in society, we will have to adopt decentralized approaches to AI and learning. Federated learning and Edge AI are two subareas of decentralized AI that are being researched by AI Sweden partners.

Decentralized AI Team

Head of Research Decentralized AI

Ather Gattami

Mats Nordlund
Head of Data Factory

Mats Nordlund

Project Manager

Erik Wilson

Project Manager Edge Lab

Kim Henriksson

Project Manager

Ebba Josefson Lindqvist

Contact us

Do you want to learn more or take part in one of our initiatives on decentralized AI?

Head of Research Decentralized AI

Ather Gattami