Fairness in Federated Learning: Challenges and Opportunities

Abstract: Federated Learning is a popular, emergent field within Machine Learning. It has advantages and challenges. It can help implement Machine Learning in tasks with low, sparse data sources or without strong central computing power. It can be a good answer for clients who do not want to share their data with the rest of the world, yet they want to benefit from the generalized performance of a global model. However, federated learning models can suffer from bias. Most of the current fairness-ensuring methods in machine learning use the relative position of the individuals within the user space to ensure the model's fairness when compared to the rest of the users. Unfortunately, it is not trivial to apply such an approach in a federated architecture. For example, how can we identify bias against a certain group (e.g. race, sex) if we don't have a global evaluation of fairness metrics? Investigating the trade-offs between privacy and fairness is an open question in Federated Learning that I will investigate in my PhD thesis. This talk provides a general overview of Federated Learning research and shows the unique challenges and solutions of fairness guarantees in a federated learning setup.

Short bio: Gergely Dániel Németh is a PhD student at ELLIS Alicante. His PhD topics are AI Ethics and Federated Learning. His supervisors are Nuria Oliver (ELLIS Alicante), Miguel Angel Lozano (University of Alicante) and Novi Quadrianto (University of Sussex). He holds a CS MSc degree from The University of Manchester and a BSc from Budapest University of Technology and Economics. His university topic was about Natural Language Processing, but he also worked on Computer Vision in a Hungarian StartUp.

Presenter: Gergely D Németh

Date: 2022-01-31 11:30 (CET)

Location: Salon de Actos Politecnica I, Carretera San Vicente del Raspeig s/n, San Vicente del Raspeig 03690, Alicante ES

Online: https://vertice.cpd.ua.es/261653