M.Sc. Ciro Benito Raggio

Motivation
Federated Learning (FL) enables the collaborative training of machine learning models across multiple institutions without sharing raw data, thereby addressing privacy concerns that are critical in healthcare scenarios. Recent advances in image-to-image (I2I) translation, such as EVA-I2I [1], have shown remarkable performance in multi-domain and multi-region translation tasks, leveraging prompt-based control to generalise even to unseen domains (zero-shot translation). However, EVA-I2I has so far been trained in a centralised setting, which limits its applicability in real-world clinical environments where data are distributed, heterogeneous, and privacy-sensitive.

How can EVA-I2I be trained in a federated setting while preserving its ability to perform multi-domain translation and generalise to unseen domains?

Student Project
The aim of this project is to integrate EVA-I2I within a FL framework and evaluate its performance in decentralised scenarios.

Notes
• Python or programming knowledge is a plus. Knowledge of medical imaging is a plus.
• All missing skills will be integrated during the first period of the thesis with dedicated sessions and goals.
• The student will have the opportunity to learn how to manage a project with SCRUM and GitFlow methodologies.

[1] L. Han et al, All-in-one medical image-to-image translation, Cell Reports Methods, Volume 5, Issue 8, 2025