3 papers
cs.LG2025
Intrinsic Training Signals for Federated Learning Aggregation
Cosimo Fiorini, Matteo Mosconi, Pietro Buzzega +2
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. While existing approaches for aggregating client-specific cla…
cs.CV2025
DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection
Chiara Cappellino, Gianluca Mancusi, Matteo Mosconi +3
Open-Vocabulary object detectors can generalize to an unrestricted set of categories through simple textual prompting. However, adapting these models to rare classes or reinforcing…
cs.LG2024
Closed-form merging of parameter-efficient modules for Federated Continual Learning
Riccardo Salami, Pietro Buzzega, Matteo Mosconi +3
Model merging has emerged as a crucial technique in Deep Learning, enabling the integration of multiple models into a unified system while preserving perfor-mance and scalability.…