Federated learning for causal inference using deep generative disentangled models
Abstract
In the context of decentralized and privacy-constrained healthcare data settings, we introduce an innovative approach to estimate individual treatment effects (ITE) via federated learning. Emphasizing the critical importance of data privacy in healthcare, especially when drawing on data from various global hospitals, we address challenges arising from data scarcity and specific treatment assignment criteria influenced by the availability of the medication of interest. Our methodology uses federated learning applied to neural network-based generative causal inference models to bridge the gap between decentralized and centralized ITE estimation on a benchmark dataset.
Scalable and Portable Federated Learning Simulation Engine
Abstract
Federated learning (FL) is one of the most promising approaches to ensure privacy in the application of data-driven techniques to sensitive information. However, the implementation of such approaches in a production environment is still an important challenge. In this paper, we present a scalable, portable, hardware-independent, model-agnostic FL Simulation Engine (FLSE) with the aim of easing the job of researchers who want to train FL models to be deployed in production environments. The FLSE offers a tool that can be used both standalone or embedded within a larger architecture, it can be deployed seamlessly and allows concurrent, scalable, and highly available V&V assessment support for FL models. The tool allows researchers to understand the behaviour, in terms of metric performance, of their proposed models in production scenarios, allowing a boost in trustworthiness towards ethical AI.
The need for multimodal health data modeling: A practical approach for a federated-learning healthcare platform
Abstract
Federated learning initiatives in healthcare are being developed to collaboratively train predictive models without the need to centralize sensitive personal data. GenoMed4All is one such project, with the goal of connecting European clinical and –omics data repositories on rare diseases through a federated learning platform. Currently, the consortium faces the challenge of a lack of well-established international datasets and interoperability standards for federated learning applications on rare diseases. This paper presents our practical approach to select and implement a Common Data Model (CDM) suitable for the federated training of predictive models applied to the medical domain, during the initial design phase of our federated learning platform. We describe our selection process, composed of identifying the consortium’s needs, reviewing our functional and technical architecture specifications, and extracting a list of business requirements. We review the state of the art and evaluate three widely-used approaches (FHIR, OMOP and Phenopackets) based on a checklist of requirements and specifications. We discuss the pros and cons of each approach considering the use cases specific to our consortium as well as the generic issues of implementing a European federated learning healthcare platform. A list of lessons learned from the experience in our consortium is discussed, from the importance of establishing the proper communication channels for all stakeholders to technical aspects related to –omics data. For federated learning projects focused on secondary use of health data for predictive modeling, encompassing multiple data modalities, a phase of data model convergence is sorely needed to gather different data representations developed in the context of medical research, interoperability of clinical care software, imaging, and –omics analysis into a coherent, unified data model. Our work identifies this need and presents our experience and a list of actionable lessons learned for future work in this direction.
Multi-Modal Analysis and Federated Learning Approach for Classification and Personalized Prognostic Assessment in Myeloid Neoplasms
Abstract
Myeloid neoplasms (MN) present clinical and molecular heterogeneity and therefore a risk-adapted treatment strategy is mandatory. In MN, classification and prognostic tools based on clinical and morphologic criteria are being complemented by introducing genomic features. The clinical implementation of next-generation classifications and prognostic systems requires the availability of a robust methodological framework together with a solution to provide access to these technologies for clinicians.
D4.1 - Hybrid Federated Learning Model
D4.1 is finally out!
This deliverable is the result of months of intense discussions on GenoMed4All’s hybrid Federated Learning framework.
Abstract
The main aim of this deliverable is to define GenoMed4All’s hybrid federated learning model specification expressed in terms of platform requirements, which have been acquired by analyzing in details the project’s objectives as well as by decomposing the stakeholders’ needs through the different use cases’ definition. It includes a description of the requirements elicitation methodology together with the set of technical requirements regarding the federated learning approach of the project’s platform. Moreover, it includes an extensive analysis of the most important federated learning framework solutions already available, and it finally defines the evaluation criteria that have helped in the selection of the most appropriate one for the project’s purposes and needs.
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