4 papers
DP-KFC: Data-Free Preconditioning for Privacy-Preserving Deep Learning
Marc Molina Van den Bosch, Riccardo Taiello, Albert Sund Aillet +3
Differentially private optimization suffers from a fundamental geometric mismatch: deep networks have highly anisotropic loss landscapes, yet DP-SGD injects isotropic noise. Second…
Federated Transformer-GNN for Privacy-Preserving Brain Tumor Localization with Modality-Level Explainability
Andrea Protani, Riccardo Taiello, Marc Molina Van Den Bosch +1
Deep learning models for brain tumor analysis require large and diverse datasets that are often siloed across healthcare institutions due to privacy regulations. We present a feder…
Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI
Lorenzo Giusti, Ole Anton Werner, Riccardo Taiello +8
We present Federation of Agents (FoA), a distributed orchestration framework that transforms static multi-agent coordination into dynamic, capability-driven collaboration. FoA intr…
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications
Riccardo Taiello, Sergen Cansiz, Marc Vesin +4
Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated…