Newest Advances on the FeatureCloud Platform for Federated Learning in Biomedicine
Authors
Probul, NiklasBakhtiari, Mohammad
Kazemi Majdabadi, Mohammad
Orbán, Balázs
Fejér, Sándor
Das, Supratim
Klemm, Julian
C Saak, Christina
K Wenke, Nina
Baumbach, Jan
Contributors
Morić, IvanaĐorđević, Valentina
Conference object (Published version)
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© 2023 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade
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AI in biomedicine has been a central research topic in recent years. Although there are many
different techniques and strategies, the majority rely on data that is of both high quality
and quantity. Despite the steady growth in the amount of data generated for patients, it is
frequently difficult to make that data useful for research because of strong restrictions through
privacy regulations such as the GDPR. Through federated learning (FL), we are able to use
distributed data for machine learning while keeping patient data inside the respective hospital.
Instead of sharing the patient data, like in traditional machine learning, each participant trains
an individual machine learning model and shares the model parameters and weights. Existing
FL frameworks, however, frequently have restrictions on certain algorithms or application
domains, and they frequently call for programming knowledge.
With FeatureCloud, we addressed these limitations and provided a user-friendly solution... for
both developers and end-users. FeatureCloud greatly simplifies the complexity of developing
federated applications and executing FL analyses in multi-institutional settings. Additionally,
it provides an app store that makes it easy for the community to publish and reuse federated
algorithms. Apps can be chained together to form pipelines and executed without programming
knowledge, making them ideal for flexible clinical applications. Apps on FeatureCloud can receive
certification from both internal and external reviewers to guarantee safety. FeatureCloud
effectively separates local components from sensitive data systems by utilizing containerization
technology, making it robust to execute in any system environment and guaranteeing data
security. To further ensure the privacy of data, FeatureCloud incorporates privacy-enhancing
technologies and complies with strict data privacy regulations, such as GDPR.
Keywords:
federated learning / biomedicine / privacy-preserving machine learning / patient privacySource:
4th Belgrade Bioinformatics Conference, 2023, 4, 49-49Publisher:
- Belgrade : Institute of molecular genetics and genetic engineering
Funding / projects:
- The FeatureCloud project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 826078. This publication reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains. This work was developed as part of the FeMAI project funded by the German Federal Ministry of Education and Research (BMBF) under grant number 01IS21079. This work was further funded by the German Federal Ministry of Education and Research (BMBF) under grant number 16DTM100A.
Note:
- Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 2023
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Institution/Community
Institut za molekularnu genetiku i genetičko inženjerstvoTY - CONF AU - Probul, Niklas AU - Bakhtiari, Mohammad AU - Kazemi Majdabadi, Mohammad AU - Orbán, Balázs AU - Fejér, Sándor AU - Das, Supratim AU - Klemm, Julian AU - C Saak, Christina AU - K Wenke, Nina AU - Baumbach, Jan PY - 2023 UR - https://belbi.bg.ac.rs/ UR - https://imagine.imgge.bg.ac.rs/handle/123456789/1991 AB - AI in biomedicine has been a central research topic in recent years. Although there are many different techniques and strategies, the majority rely on data that is of both high quality and quantity. Despite the steady growth in the amount of data generated for patients, it is frequently difficult to make that data useful for research because of strong restrictions through privacy regulations such as the GDPR. Through federated learning (FL), we are able to use distributed data for machine learning while keeping patient data inside the respective hospital. Instead of sharing the patient data, like in traditional machine learning, each participant trains an individual machine learning model and shares the model parameters and weights. Existing FL frameworks, however, frequently have restrictions on certain algorithms or application domains, and they frequently call for programming knowledge. With FeatureCloud, we addressed these limitations and provided a user-friendly solution for both developers and end-users. FeatureCloud greatly simplifies the complexity of developing federated applications and executing FL analyses in multi-institutional settings. Additionally, it provides an app store that makes it easy for the community to publish and reuse federated algorithms. Apps can be chained together to form pipelines and executed without programming knowledge, making them ideal for flexible clinical applications. Apps on FeatureCloud can receive certification from both internal and external reviewers to guarantee safety. FeatureCloud effectively separates local components from sensitive data systems by utilizing containerization technology, making it robust to execute in any system environment and guaranteeing data security. To further ensure the privacy of data, FeatureCloud incorporates privacy-enhancing technologies and complies with strict data privacy regulations, such as GDPR. PB - Belgrade : Institute of molecular genetics and genetic engineering C3 - 4th Belgrade Bioinformatics Conference T1 - Newest Advances on the FeatureCloud Platform for Federated Learning in Biomedicine EP - 49 SP - 49 VL - 4 UR - https://hdl.handle.net/21.15107/rcub_imagine_1991 ER -
@conference{ author = "Probul, Niklas and Bakhtiari, Mohammad and Kazemi Majdabadi, Mohammad and Orbán, Balázs and Fejér, Sándor and Das, Supratim and Klemm, Julian and C Saak, Christina and K Wenke, Nina and Baumbach, Jan", year = "2023", abstract = "AI in biomedicine has been a central research topic in recent years. Although there are many different techniques and strategies, the majority rely on data that is of both high quality and quantity. Despite the steady growth in the amount of data generated for patients, it is frequently difficult to make that data useful for research because of strong restrictions through privacy regulations such as the GDPR. Through federated learning (FL), we are able to use distributed data for machine learning while keeping patient data inside the respective hospital. Instead of sharing the patient data, like in traditional machine learning, each participant trains an individual machine learning model and shares the model parameters and weights. Existing FL frameworks, however, frequently have restrictions on certain algorithms or application domains, and they frequently call for programming knowledge. With FeatureCloud, we addressed these limitations and provided a user-friendly solution for both developers and end-users. FeatureCloud greatly simplifies the complexity of developing federated applications and executing FL analyses in multi-institutional settings. Additionally, it provides an app store that makes it easy for the community to publish and reuse federated algorithms. Apps can be chained together to form pipelines and executed without programming knowledge, making them ideal for flexible clinical applications. Apps on FeatureCloud can receive certification from both internal and external reviewers to guarantee safety. FeatureCloud effectively separates local components from sensitive data systems by utilizing containerization technology, making it robust to execute in any system environment and guaranteeing data security. To further ensure the privacy of data, FeatureCloud incorporates privacy-enhancing technologies and complies with strict data privacy regulations, such as GDPR.", publisher = "Belgrade : Institute of molecular genetics and genetic engineering", journal = "4th Belgrade Bioinformatics Conference", title = "Newest Advances on the FeatureCloud Platform for Federated Learning in Biomedicine", pages = "49-49", volume = "4", url = "https://hdl.handle.net/21.15107/rcub_imagine_1991" }
Probul, N., Bakhtiari, M., Kazemi Majdabadi, M., Orbán, B., Fejér, S., Das, S., Klemm, J., C Saak, C., K Wenke, N.,& Baumbach, J.. (2023). Newest Advances on the FeatureCloud Platform for Federated Learning in Biomedicine. in 4th Belgrade Bioinformatics Conference Belgrade : Institute of molecular genetics and genetic engineering., 4, 49-49. https://hdl.handle.net/21.15107/rcub_imagine_1991
Probul N, Bakhtiari M, Kazemi Majdabadi M, Orbán B, Fejér S, Das S, Klemm J, C Saak C, K Wenke N, Baumbach J. Newest Advances on the FeatureCloud Platform for Federated Learning in Biomedicine. in 4th Belgrade Bioinformatics Conference. 2023;4:49-49. https://hdl.handle.net/21.15107/rcub_imagine_1991 .
Probul, Niklas, Bakhtiari, Mohammad, Kazemi Majdabadi, Mohammad, Orbán, Balázs, Fejér, Sándor, Das, Supratim, Klemm, Julian, C Saak, Christina, K Wenke, Nina, Baumbach, Jan, "Newest Advances on the FeatureCloud Platform for Federated Learning in Biomedicine" in 4th Belgrade Bioinformatics Conference, 4 (2023):49-49, https://hdl.handle.net/21.15107/rcub_imagine_1991 .