AI Fairness in Federated Learning
Gergely Dániel Németh (Ph.D. Student)
Federated Learning is a rapidly emerging approach to solve privacy problems regarding Machine Learning applications. Fairness in Artificial Intelligence is also a field that gained awareness in recent years. At the intersection of the two fields, we can find new solutions for problems that exist in centralized machine learning, but also new problems emerge that are unique to the federated learning training architectures. In my PhD, I will address these and work towards fair, human-centric federated learning.
|Primary Host:||Nuria Oliver (DataPop Alliance, Vodafone Institute & The Spanish Royal Academy of Engineering)|
|Exchange Host:||Novi Quadrianto (University of Sussex)|
|PhD Duration:||01 November 2021 - 31 July 2024|
|Exchange Duration:||01 January 2023 - 30 June 2023 - Ongoing|