Federated learning in biomedical applications

My research is on the theory and practical use-cases of federated learning (FL), with a special focus in healthcare applications. Current topics include:

• Studying the statistical properties of aggregation mechanisms and clients’ sampling methods in heterogeneity FL setting
• Investigating robust FL methods with respect to clients’ heterogeneity and attacks
• Developing the FL software Fed-BioMed an open source project focused on empowering biomedical research using non-centralized approaches for statistical analysis and machine learning

Selected software and papers:

• Fed-BioMed: federated learning in healthcare; Cremonesi et al, 2024
• Freerider attacks in federated learning;Fraboni et al, AISTATS 2021
• Clustered Sampling in federated learning; Fraboni et al, ICML 2021