Summary of A Secure and Trustworthy Network Architecture For Federated Learning Healthcare Applications, by Antonio Boiano et al.
A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications
by Antonio Boiano, Marco Di Gennaro, Luca Barbieri, Michele Carminati, Monica Nicoli, Alessandro Redondi, Stefano Savazzi, Albert Sund Aillet, Diogo Reis Santos, Luigi Serio
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The TRUSTroke project aims to develop a Federated Learning (FL) network infrastructure for ischemic stroke prediction in healthcare settings. This paper outlines the proposed architecture, which adopts a client-server model with a central Parameter Server (PS). A Docker-based design is introduced for client nodes, providing flexibility in clinical implementations. The impact of different communication protocols (HTTP or MQTT) on FL operation is analyzed, with MQTT selected for its suitability. A control plane supports main operations required by FL processes. The paper concludes with an analysis of security aspects and proposed mitigation strategies to increase trustworthiness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The TRUSTroke project uses a special type of machine learning called Federated Learning to help doctors predict when someone might have a stroke. This means that data from many different places can be used without sharing the actual information, which is important for keeping people’s health information private. The team designed a special way for computers to talk to each other and share information in a safe and secure way. They also looked at how fast or slow the communication between computers affects the accuracy of the predictions. |
Keywords
» Artificial intelligence » Federated learning » Machine learning
