Hi! PARIS Seminar – Jan Ramon, 27 April 2023

Hi! PARIS is pleased to propose an exceptional seminar of Jan Ramon.

Thanks to Aymeric Dieuleveut, Hi! PARIS Fellowship holder 2021 and Assistant Professor in Statistics at École polytechnique, we have the pleasure to welcome Jan Ramon for an exceptional seminar, entitled “Towards private and practical federated learning”.

Thursday 27 April 2023, 12.15-1.15pm
Telecom Paris, Palaiseau (room 0A128)
On site + Zoom

Towards private and practical federated learning

While exploiting the large amounts of available data has significant economic and societal benefits, in recent years there has been a growing awareness of the privacy risks.
Many researchers are therefore looking at settings where data remains at the premises of the data owners and these data owners learn a statistical model together without revealing their own data.  The best known strategies involve both encryption and noise addition, in particular encryption helps to hide intermediate results which otherwise may have leaked information unnecessarily, and noise addition ensures that no sensitive information can be inferred from the output of an algorithm.  This however poses several new challenges.  First, the decentralization and encryption have a significant impact on the computation and communication cost.  Second, while differential privacy has emerged as a gold standard privacy notion it is insufficiently fine-grained to yield the best privacy-utility trade-off. 
In this presentation, I explain in more depth these challenges and discuss recent results and ongoing work aiming to overcome these hurdles from the point of view of the objectives of the TRUMPET project, a recently started Horizon Europe project which wants to provide groups of hospitals a platform for strongly secure and privacy-preserving but still efficient federated learning.

Jan Ramon 

Jan Ramon is a senior researcher in the MAGNET (Machine learniNG in large-scale information NETworks) group at Inria-Lille, France. His research interests include data mining and machine learning on graph-structured data, algorithmic and statistical aspects of graph-structured data, privacy-preserving techniques and applications in biomedical domains and traffic.

Keywords: computational statistics ; probabilistic programming ; automatic differentiation ; natural language processing