AI Folk
Resource Management for Distributed AI

Implementing a vision in which intelligent agents perceive and act in an environment while searching for, exchanging, annotating, and improving machine learning models, forming a culture based on experience and interaction.

About the project Project objectives Outputs and Papers AI Folk Team


About the project

This project is funded by a grant of the Ministry of Research, Innovation and Digitization, CNCS - UEFISCDI, project number PN-III-P1-1.1-TE-2021-1422, within PNCDI III.

Project duration: May 2022 - May 2024


Our research goal is to develop a knowledge model and an interaction protocol which allow a system formed of multiple actors -- human, software, or organizational -- to find, use and share improvements on machine learning resources. Such resources can be datasets, models, or experiences.

We propose the development of the AI Folk framework and methodology, at the intersection of machine learning, knowledge management, and multi-agent systems. It comprises tools and methods that allow the management and discovery of ML-related resources in a distributed system. Federated learning has yet to achieve maturity as a field of study and this is a novel approach which assumes an open system and a variety of resources. We believe that this approach will lead to an advance in the state of the art and will help in the development of standards for open and distributed artificial intelligence.

The objectives for this project are to:



Papers with the project acknowledgement



At the end of the project, we will have developed the following resources: The 2022 report introduces: Download the report here

The 2023 report presents: Download the report here


The project is developed by a team of young researchers ar University Politehnica of Bucharest:

Andrei Olaru, PhD, associate professor -- project coordinator
Alexandru Sorici, PhD, Lecturer
David-Traian Iancu, PhD, Lecturer
Mihai Nan, PhD, Lecturer