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Marcos de aprendizaje federado: calidad e interoperabilidad para la investigación biomédica

María Chavero-Diez1,2, Carles Hernandez-Ferrer1, Laia Codó1

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Este resumen es generado por máquina.

Los marcos de aprendizaje federado en la investigación biomédica muestran potencial pero necesitan mejoras en interoperabilidad y características de privacidad. Mejorar estos aspectos es crucial para un uso sostenible y escalable en entornos de datos sensibles.

Palabras clave:
aprendizaje federadoinvestigación biomédicainteroperabilidadprivacidad de datossoftware de investigación

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Área de la Ciencia:

  • Investigación biomédica
  • Ciencias de la computación
  • Privacidad de datos

Sus antecedentes:

  • Las estrictas regulaciones de datos en la investigación biomédica dificultan el intercambio de datos.
  • El aprendizaje federado (FL) ofrece una solución para el análisis colaborativo sin centralizar datos sensibles.
  • Los marcos de FL existentes requieren evaluación para determinar su idoneidad en este dominio.

Objetivo del estudio:

  • Evaluar la sostenibilidad, flexibilidad y usabilidad de los marcos actuales de aprendizaje federado para la investigación biomédica.
  • Identificar brechas en las funcionalidades y escalabilidad de los marcos.
  • Evaluar los marcos según los principios FAIR (Encontrabilidad, Accesibilidad, Interoperabilidad, Reutilización) para el software de investigación.

Principales métodos:

  • Análisis sistemático de la literatura de marcos de aprendizaje federado.
  • Evaluación según los principios FAIR para el software de investigación.
  • Comparación de los casos de uso reportados con las funcionalidades del marco.

Principales resultados:

  • Los marcos generalmente obtienen buenos resultados en encontrabilidad y reutilización.
  • Existen limitaciones significativas en la interoperabilidad entre los marcos y con otras bibliotecas de software.
  • La integración limitada de técnicas de preservación de la privacidad y una prevalencia de arquitecturas horizontales pueden obstaculizar la escalabilidad.
  • Existe un potencial de aplicabilidad más amplia a pesar del desarrollo especializado.

Conclusiones:

  • Los marcos de aprendizaje federado requieren mejoras en interoperabilidad y flexibilidad para aplicaciones biomédicas.
  • Es necesaria una mayor adopción de técnicas de preservación de la privacidad para un aprendizaje federado escalable y seguro.
  • Los futuros marcos deben priorizar la modularidad y una mayor compatibilidad para satisfacer las demandas de la investigación biomédica compleja.