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Federated Learning Life Cycle

This section provides a structured overview of the Federated Learning (FL) life cycle, organized around four key elements essential for secure, distributed model development:

  • Governance: Policies, compliance, and decision-making frameworks that ensure responsible data and model management throughout the FL process.
  • Infrastructure: The technical and organizational setup required to support federated learning, including hardware, software, and network resources.
  • Wrangling: The processes of preparing, cleaning, harmonizing, and transforming data to make it suitable for federated analysis.
  • Analysis: The methods and tools used to train, evaluate, and interpret federated models, turning distributed data into actionable insights.
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Plan & Govern
Plan & Govern
Establishing the foundations for data sharing
Establishing the foundations fo... </g>

Enable Infrastructure

Enable Infrastructure

Defining and aligning the technical foundation for data use

Defining and aligning the technical... </g>
Enhance & Wrangle Data
Enhance & Wrangle Data
Making multi-site data comparable and analysis-ready
Making multi-site data comparable a...

Analyse    Shared Data

Analyse   Shared Data
Deriving insights from the prepared, multi-site data
Deriving insights from the prepared, m... </svg> </div> ## Technical Readiness Levels for Data and Analysis in Healthcare **TRL 1 – Clinical or Scientific Concept Identified** An early-stage healthcare hypothesis or need is identified (e.g., understanding disease burden, evaluating treatment pathways). The idea is conceptual, grounded in literature or expert opinion, with no defined analytical approach. **TRL 2 – Analytical Approach Formulated** Initial discussions shape how data could support the healthcare question. This may include identifying potential data sources (e.g., EHRs, claims, registries) and suggesting broad analytical methods (e.g., cohort studies, ML models). No data extraction or modeling occurs yet. **TRL 3 – Feasibility Demonstrated Using Sample Data** Data availability and quality are assessed using small samples or public datasets. A proof of concept is developed—such as a basic model or summary statistics—to test initial assumptions. Clinical stakeholders are often engaged to refine the scope. **TRL 4 – Data Pipeline and Preliminary Model Built in Controlled Setting** A prototype data pipeline is constructed using real healthcare data (e.g., EHRs, lab results). Early models or analytics are tested, but only within a sandbox or test environment. Regulatory and privacy constraints are considered at this stage. **TRL 5 – Model or Analytical Tool Validated in Relevant Healthcare Setting** The solution is applied to a real clinical dataset relevant to the target population. Issues like data completeness, coding variability (e.g., ICD/LOINC/SNOMED), and confounders are addressed. Early validation is performed (e.g., comparison to gold standards or known outcomes). **TRL 6 – Prototype Used in Pilot Setting with Clinical Input** An operational prototype (such as a risk score, decision support tool, or population health dashboard) is tested in collaboration with clinicians or care teams. The model outputs begin influencing early-stage decision-making or workflow evaluations. **TRL 7 – Deployment in Clinical or Operational Environment** The solution is integrated into a real-world healthcare system, such as being embedded in EHR workflows or clinical dashboards. The tool supports care decisions, policy evaluation, or operational planning. Usage, safety, and performance are monitored closely. **TRL 8 – Fully Qualified Solution with Governance and Monitoring** The analytical solution is validated across multiple real-world datasets and meets clinical, regulatory, and operational standards. Robust governance is in place for model updates, bias monitoring, data provenance, and explainability. **TRL 9 – Real-World Evidence Harnessed for Practice and Policy** The tool generates accepted real-world evidence used in decision-making at scale—supporting clinical guidelines, reimbursement models, public health strategies, or regulatory submissions. Results are peer-reviewed or published, and the solution is maintained in live operations.

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Contributors
Ashkan Pirmani Editor

KU Leuven / University of UHasselt