Výsledky vyhledávání - "Debora Caldarola"

Akademický článek

Accelerating Federated Learning via Sequential Training of Grouped Heterogeneous Clients

Autor: Andrea Silvi, Andrea Rizzardi, Debora Caldarola, Barbara Caputo, Marco Ciccone

Publikováno v: IEEE Access, Vol 12, Pp 57043-57058 (2024)

Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistica

Externí odkaz: https://doaj.org/article/1be9762d70e546808673fc283b5ee9c5

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Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning

Autor: Donald Shenaj, Eros Fani, Marco Toldo, Debora Caldarola, Antonio Tavera, Umberto Michieli, Marco Ciccone, Pietro Zanuttigh, Barbara Caputo

Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the collected data. However, most of the existing works on FL unrealisticall

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::240c9fb3a5e11c162e45c77d7a4e113c
http://arxiv.org/abs/2210.02326

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FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

Autor: Lidia Fantauzzo, Eros Fani, Debora Caldarola, Antonio Tavera, Fabio Cermelli, Marco Ciccone, Barbara Caputo

Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. However, it operates on sensible data collected from the users' cars; thus

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b3da7166d0a2b44ce3b50b53263015b
http://hdl.handle.net/11583/2970566

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Cluster-driven Graph Federated Learning over Multiple Domains

Autor: Debora Caldarola, Massimiliano Mancini, Fabio Galasso, Barbara Caputo, Emanuele Rodolà, Marco Ciccone

Publikováno v: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
CVPR Workshops

Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients). The central model has no direct access to the data, but only to the updates

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e8bd7731636444974105f1f9b44dee4
http://hdl.handle.net/11583/2898498

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