Federated learning and beyond

Abstract

 Federated Learning has been proposed to develop better AI systems without compromising the privacy of final users and the legitimate interests of private companies. Federated learning can be coupled with other learning techniques, such as continual and adversarial learning, and could be a real game-changer for analyzing inherently distributed critical data. The project aims to explore the boundary of distributed and federated learning techniques.

General Info

This project will be carried out in the Department of Computer Science.

This PhD project is part of a call for 5 fully funded PhD scholarships in the framework of the PhD in Modeling and Data Science https://dottorato-mds.campusnet.unito.it/do/home.pl at the University of Turin (Italy). The call will open on 28 April 2022 (date TBC). The scholarship is for three years, starting in October 2022.

The PhD program is interdisciplinary, and it involves branches of mathematics, informatics, economics, statistics, and physics.

All interested candidates should submit their application online via the link https://www.phd.unito.it/do/home.pl/View?doc=Submitting_your_application.html. Deadline for applying is 30 May 2022 (date TBC). Notice that the application requires two reference letters, which should be submitted via the same link by the referees before the application deadline. The referees will be able to submit their letters only after the candidate has input all the required information and closed their (part of the) application. If the letters are not submitted by the deadline the application will not be valid.

For more information, do not hesitate to contact the supervisor. More information, including the official call and all relevant (confirmed) deadlines, can also be found here https://dottorato-mds.campusnet.unito.it/do/home.pl/View?doc=/content/Admission.html.

The call for applications is available at the page https://www.dottorato.unito.it/do/home.pl/View?doc=Bando_XXXVIII_ciclo.html. 

Admission details and the list of projects can be found here https://www.dottorato.unito.it/do/documenti.pl/ShowFile?_id=g3ot;field=file;key=JbqgKqg0lroYaYMa8BXEFYBVz1z8MXSdAKr7fGFtPR2;t=3298.