A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929318222888960 |
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| author | Boiano, Antonio Di Gennaro, Marco Barbieri, Luca Carminati, Michele Nicoli, Monica Redondi, Alessandro Savazzi, Stefano Aillet, Albert Sund Santos, Diogo Reis Serio, Luigi |
| author_facet | Boiano, Antonio Di Gennaro, Marco Barbieri, Luca Carminati, Michele Nicoli, Monica Redondi, Alessandro Savazzi, Stefano Aillet, Albert Sund Santos, Diogo Reis Serio, Luigi |
| contents | Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic stroke prediction. This paper provides an overview of the TRUSTroke FL network infrastructure. The proposed architecture adopts a client-server model with a central Parameter Server (PS). We introduce a Docker-based design for the client nodes, offering a flexible solution for implementing FL processes in clinical settings. The impact of different communication protocols (HTTP or MQTT) on FL network operation is analyzed, with MQTT selected for its suitability in FL scenarios. A control plane to support the main operations required by FL processes is also proposed. The paper concludes with an analysis of security aspects of the FL architecture, addressing potential threats and proposing mitigation strategies to increase the trustworthiness level. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11698 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications Boiano, Antonio Di Gennaro, Marco Barbieri, Luca Carminati, Michele Nicoli, Monica Redondi, Alessandro Savazzi, Stefano Aillet, Albert Sund Santos, Diogo Reis Serio, Luigi Artificial Intelligence Distributed, Parallel, and Cluster Computing Federated Learning (FL) has emerged as a promising approach for privacy-preserving machine learning, particularly in sensitive domains such as healthcare. In this context, the TRUSTroke project aims to leverage FL to assist clinicians in ischemic stroke prediction. This paper provides an overview of the TRUSTroke FL network infrastructure. The proposed architecture adopts a client-server model with a central Parameter Server (PS). We introduce a Docker-based design for the client nodes, offering a flexible solution for implementing FL processes in clinical settings. The impact of different communication protocols (HTTP or MQTT) on FL network operation is analyzed, with MQTT selected for its suitability in FL scenarios. A control plane to support the main operations required by FL processes is also proposed. The paper concludes with an analysis of security aspects of the FL architecture, addressing potential threats and proposing mitigation strategies to increase the trustworthiness level. |
| title | A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications |
| topic | Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2404.11698 |