The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond

Matschinske, Julian; Späth, Julian; Nasirigerdeh, Reza; Torkzadehmahani, Reihaneh; Hartebrodt, Anne; Orbán, Balázs; Fejér, Sándor; Zolotareva, Olga A.; Bakhtiari, M.; Bihari, Béla; Bloice, Marcus Daniel; Donner, Nina C.; Fdhila, Walid; Frisch, Tobias; Hauschild, Anne-Christin; Heider, Dominik; Holzinger, Andreas; Hötzendorfer, Walter; Hospes, Jan; Kacprowski, Tim; Kastelitz, Markus; List, Markus; Mayer, Rudolf; Moga, Mónika; Müller, Heimo; Pustozerova, Anastasia; Röttger, Richard; Saranti, Anna; Schmidt, Harald; Tschohl, Christof; Wenke, Nina K.; Baumbach, Jan

doi:10.48550/arxiv.2105.05734

Cited by 2 publications

(1 citation statement)

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“…JIP provides containerized tools for Federated Learning, and many institutions have committed to testing JIP for use cases in the coming years. [90] provides another framework with multiple objectives and use cases. Here, the authors proposed a "marketplace" approach to federated learning: it provides the infrastructure and other computational resources for 3rd party applications to run in a Secure Multiparty Computation system; there, for sake of example, multiple computational tasks related to cancer research (from data normalization to Kaplan-Mayer analysis and COX regression) are treated as "Apps" and deployed into a secure and distributed environment.…”

Section: Federated Learning Frameworkmentioning

confidence: 99%

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Chowdhury

Kassem

Padoy

et al. 2022

Lecture Notes in Computer Science

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Machine learning has revolutionized every facet of human life, while also becoming more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to annotate and difficult to collect centrally due to privacy concerns. Recently, Federated Learning has been proposed as a distributed learning technique to alleviate many of these privacy concerns by providing a decentralized training paradigm for models using large, distributed data. This new approach has become the defacto way of building machine learning models in multiple industries (e.g. edge computing, smartphones). Due to its strong potential, Federated Learning is also becoming a popular training method in healthcare, where patient privacy is of paramount concern. In this paper we performed an extensive literature review to identify state-of-the-art Federated Learning applications for cancer research and clinical oncology analysis. Our objective is to provide readers with an overview of the evolving Federated Learning landscape, with a focus on applications and algorithms in oncology space. Moreover, we hope that this review will help readers to identify potential needs and future directions for research and development.

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Section: Federated Learning Frameworkmentioning

confidence: 99%

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Chowdhury

Kassem

Padoy

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning

Holzinger,

Saranti,

Hauschild

et al. 2023

Lecture Notes in Computer Science

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We explore the integration of domain knowledge graphs into Deep Learning for improved interpretability and explainability using Graph Neural Networks (GNNs). Specifically, a protein-protein interaction (PPI) network is masked over a deep neural network for classification, with patient-specific multi-modal genomic features enriched into the PPI graph’s nodes. Subnetworks that are relevant to the classification (referred to as “disease subnetworks”) are detected using explainable AI. Federated learning is enabled by dividing the knowledge graph into relevant subnetworks, constructing an ensemble classifier, and allowing domain experts to analyze and manipulate detected subnetworks using a developed user interface. Furthermore, the human-in-the-loop principle can be applied with the incorporation of experts, interacting through a sophisticated User Interface (UI) driven by Explainable Artificial Intelligence (xAI) methods, changing the datasets to create counterfactual explanations. The adapted datasets could influence the local model’s characteristics and thereby create a federated version that distils their diverse knowledge in a centralized scenario. This work demonstrates the feasibility of the presented strategies, which were originally envisaged in 2021 and most of it has now been materialized into actionable items. In this paper, we report on some lessons learned during this project.

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The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond

Cited by 2 publications

References 0 publications

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

A Review of Medical Federated Learning: Applications in Oncology and Cancer Research

Human-in-the-Loop Integration with Domain-Knowledge Graphs for Explainable Federated Deep Learning

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