A Mathematical Theory of Federated Learning (TRUST-FELT)
Description of the granted funding
 Artificial intelligence (AI) is integral to our daily lives, influencing our job search, housing, and relationships. Many AI services are powered by federated learning (FL) systems providing tailored predictions on interests like job offers, dating, and music videos. Despite the usefulness of FL systems, there is increasing evidence for their potentially harmful effects, such as boosting addictive user behavior or even genocide.This project breaks ground for trustworthy FL, shifting the focus of current FL research towards a more human-centric perspective. Besides the computational and statistical properties of FL systems, this project emphasizes important design criteria for trustworthy AI. 
Show moreStarting year
 2024 
Granted funding
 Alexander Jung 
 498 000 € 
Funder
 Jane and Aatos Erkko Foundation 
Call
Other information
Funding decision number
 A835 
Themes
 Tekniikka 
Keywords
 113 Tietojenkäsittely ja informaatiotieteet 
Identified topics
 artificial intelligence,  machine learning