Federated Learning to Safeguard Patients Data: A Medical Image Retrieval Case

Singh Gurtaj; Violi Vincenzo; Fisichella Marco

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Federated Learning to Safeguard Patients Data: A Medical Image Retrieval Case

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Healthcare data are distributed and confidential, making it difficult to use centralized automatic diagnostic techniques. For example, different hospitals hold the electronic health records (EHRs) of different patient populations; however, transferring this data between hospitals is difficult due to the sensitive nature of the information. This presents a significant obstacle to the development of efficient and generalizable analytical methods that require a large amount of diverse Big Data. Federated learning allows multiple institutions to work together to develop a machine learning algorithm without sharing their data. We conducted a systematic study to analyze the current state of FL in the healthcare industry and explore both the limitations of this technology and its potential. Organizations share the parameters of their models with each other. This allows them to reap the benefits of a model developed with a richer data set while protecting the confidentiality of their data. Standard methods for large-scale machine learning, distributed optimization, and privacy-friendly data analytics need to be fundamentally rethought to address the new problems posed by training on diverse networks that may contain large amounts of data. In this article, we discuss the particular qualities and difficulties of federated learning, provide a comprehensive overview of current approaches, and outline several directions for future work that are relevant to a variety of research communities. These issues are important to many different research communities.

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《Big Data and Cognitive Computing》 |2023年第1期|共16页
作者
Singh Gurtaj; Violi Vincenzo; Fisichella Marco;
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作者单位

Leibniz Univ Hannover;

Univ Pisa;

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原文格式 PDF
正文语种英语
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关键词
federated learning; health; privacy preserving; PRIVACY;

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1. University Mediterranea of Reggio Calabria Researchers Provide New Data on Machine Learning (Federated Learning to Safeguard Patients Data: A Medical Image Retrieval Case) [J] . Robotics & Machine Learning Daily News . 2023,第12期

机译：University Mediterranea of Reggio Calabria Researchers Provide New Data on Machine Learning (Federated Learning to Safeguard Patients Data: A Medical Image Retrieval Case)
2. Study Data from Huazhong University of Science and Technology Provide New Insights into Communications and Networks (Safeguarding cross-silo federated learning with local differential privacy) [J] . Network Daily News . 2022,第9期

机译：Study Data from Huazhong University of Science and Technology Provide New Insights into Communications and Networks (Safeguarding cross-silo federated learning with local differential privacy)
3. Investigators from Swiss Federal Institute of Technology Lausanne (EPFL) Report New Data on Machine Learning (Phase Retrieval: From Computational Imaging To Machine Learning: a Tutorial) [J] . Robotics & Machine Learning Daily News . 2023,第17期

机译：Investigators from Swiss Federal Institute of Technology Lausanne (EPFL) Report New Data on Machine Learning (Phase Retrieval: From Computational Imaging To Machine Learning: a Tutorial)
4. Patient Data Viewer: An Android application for healthcare [C] . Challa Samyuktha, Geethakumari G., Prasad C. S. N. 2011 Annual IEEE India Conference : Engineering Sustainable Solutions . 2011

机译：Patient Data Viewer：用于医疗保健的Android应用程序
5. Learning on relevance feedback in content-based image retrieval. [O] . 2004

机译：Learning on relevance feedback in content-based image retrieval.

Federated Learning to Safeguard Patients Data: A Medical Image Retrieval Case

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