Federated machine learning in data-protection-compliant research

Brauneck, Alissa; Schmalhorst, Louisa; Majdabadi, Mohammad Mahdi Kazemi; Bakhtiari, Mohammad; Völker, Uwe; Saak, Christina C.; Baumbach, Jan; Baumbach, Linda; Buchholtz, Gabriele

doi:10.1038/s42256-022-00601-5

Cited by 14 publications

(12 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the real-world microbiome data set are promising and show that FL might be an accelerator in microbiome research and the analysis of time-to-event microbiome data sets. Using FL combined with additive secret sharing, our approach can be currently considered GDPR compliant and, therefore, practically usable in real clinical time-to-event studies [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

“…Especially in combination with privacy-enhancing techniques (PETs), model parameters can be exchanged securely, such that a global aggregator or potential attacker cannot even see the local parameters of each participant [ 11 ]. This secure exchange of model parameters is necessary to comply with the GDPR, as even local models can be considered personal data [ 12 ]. Therefore, FL enables the training on a significantly larger data set compared with single-institution scenarios.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Späth,

Sewald,

Probul

et al. 2024

JMIR AI

Self Cite

View full text Add to dashboard Cite

Background Central collection of distributed medical patient data is problematic due to strict privacy regulations. Especially in clinical environments, such as clinical time-to-event studies, large sample sizes are critical but usually not available at a single institution. It has been shown recently that federated learning, combined with privacy-enhancing technologies, is an excellent and privacy-preserving alternative to data sharing. Objective This study aims to develop and validate a privacy-preserving, federated survival support vector machine (SVM) and make it accessible for researchers to perform cross-institutional time-to-event analyses. Methods We extended the survival SVM algorithm to be applicable in federated environments. We further implemented it as a FeatureCloud app, enabling it to run in the federated infrastructure provided by the FeatureCloud platform. Finally, we evaluated our algorithm on 3 benchmark data sets, a large sample size synthetic data set, and a real-world microbiome data set and compared the results to the corresponding central method. Results Our federated survival SVM produces highly similar results to the centralized model on all data sets. The maximal difference between the model weights of the central model and the federated model was only 0.001, and the mean difference over all data sets was 0.0002. We further show that by including more data in the analysis through federated learning, predictions are more accurate even in the presence of site-dependent batch effects. Conclusions The federated survival SVM extends the palette of federated time-to-event analysis methods by a robust machine learning approach. To our knowledge, the implemented FeatureCloud app is the first publicly available implementation of a federated survival SVM, is freely accessible for all kinds of researchers, and can be directly used within the FeatureCloud platform.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Späth,

Sewald,

Probul

et al. 2024

JMIR AI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many of these techniques can be applied to differential models (19). To increase privacy, a layer of privacy mechanism such as differential privacy, hashing and cryptographic methods (6) over the models is applied while distributing the models (20)(21)(22)(23)(24)(25)(26). In our recent publication, we analysed multiple FL schemes based on ANN using electrocardiograms as the dataset (27).…”

Section: Related Workmentioning

confidence: 99%

Development and validation of random-forest based federated learning algorithms for delirium prediction using electronic medical records from eleven hospitals in Austria: a retrospective study

Veeranki

2024

Preprint

View full text Add to dashboard Cite

Background Machine learning models have shown great potential in preventive medicine but require large datasets, which is a challenge due to strict privacy regulations in the healthcare sector. Federated learning is an approach that enables collaboration between institutions while preserving data privacy. The focus today in research is on developing federated learning methods using artificial neural networks. In this study, we aimed to contribute federated learning modelling methods applied for random forests with a use case of predicting delirium in hospitalised patients using data from multiple hospitals. Methods We collected data from 11 hospitals, including 29,479 patients and 627 features. We trained random forest models with each hospital’s data and a general model using all hospitals data. We developed federated learning models by averaging the predictions of the individual hospital models, with different schemes based on the number of samples, positive cases, minority cases and maximum possible diversity and evaluated the models using area under the receiver operating characteristic curve (AUROC) as a performance measure. Results The general model outperformed all the other models with an AUROC of 0.854 [0.849-0.860]. Models trained on data from single hospitals varied in performance with AUROC from 0.626 to 0.828. Models from hospitals with large datasets performed better than that of small hospitals. The general model outperformed all the other models with an AUROC of 0.854. Federated learning models performed better than individual models. Unweighted averaging performed worst with an AUROC of 0.793 [0.782-0.805]. Among the weighted averaging schemes, the number of positive cases performed the best with an AUROC of 0.843 [0.838-0.846], followed by minority class (AUROC=0.840 [0.836-0.845]), maximum possible diversity (AUROC=0.836 [0.830-0.841]) and number of samples (AUROC=0.830 [0.819-0.841]). Conclusions Results suggest that federated learning models can perform better than hospital-specific models in some cases, especially hospitals with limited data. In case of datasets of different size, we suggest weighted averaging based on the number of samples. If the datasets are class imbalanced, maximum possible diversity should also be considered. Additionally, federated learning models are consistent and stable in performance compared to hospital specific models.

show abstract

“…By addressing software security risks, organizations can reduce the likelihood and impact of security breaches and safeguard their software assets. Some of the key aspects considered in software security include threat modeling [99]- [102], secure design and development [103], authentication and authorization [104], data protection [105], regular updates and patching [106], secure configuration and deployment, secure testing, incident response and recovery, user education and awareness, compliance and regulations.…”

Section: Ensuring Availability and Reliabilitymentioning

confidence: 99%

Theory and practice in secure software development lifecycle: A comprehensive survey

Otieno¹,

Odera²,

Ounza³

2023

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

Software development security refers to the practice of integrating security measures and considerations throughout the software development lifecycle to ensure the confidentiality, integrity, and availability of software systems. It involves identifying, mitigating, and eliminating security vulnerabilities and threats that could be exploited by attackers. The goal of this paper is to survey the various concepts and methodologies directed towards software security, and the identification of any missing gaps. Based on the findings, it is noted that the development of secure software requires a proactive and comprehensive approach. It begins with establishing secure design principles and incorporating security requirements from the initial stages of development. Here, secure coding practices, such as input validation, output encoding, and secure authentication and authorization mechanisms, are employed to prevent common security vulnerabilities. In addition, regular security testing, including penetration testing and vulnerability scanning, helps identify and address potential weaknesses in the software. Normally, code reviews and security audits are conducted to ensure adherence to secure coding practices and identify any security flaws. It is important that security training and awareness programs be provided to developers and other stakeholders to foster a security-conscious culture. To minimize potential vulnerabilities, secure configuration management, which involves properly configuring servers, networks, and dependencies may be utilized. On the other hand, regular updates and patching are essential to address known security vulnerabilities in software components. To guide their software development security practices, organizations may follow established security standards and frameworks such as ISO 27001 or NIST Cybersecurity Framework. By prioritizing software development security, organizations can protect sensitive data, prevent unauthorized access, and mitigate the risk of security breaches and incidents. In the long run, this helps build trust with users and stakeholders, enhances the reputation of the software, and reduces the potential impact of security incidents on the organization.

show abstract

Federated machine learning in data-protection-compliant research

Cited by 14 publications

References 17 publications

Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Development and validation of random-forest based federated learning algorithms for delirium prediction using electronic medical records from eleven hospitals in Austria: a retrospective study

Theory and practice in secure software development lifecycle: A comprehensive survey

Contact Info

Product

Resources

About