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Updated: Feb 14, 2026

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Optimización de la Protección de la Privacidad para Plataformas en la Nube Basada en Aprendizaje Federado y Cifrado

Jing Wang1, Yun Wang1

  • 1School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.

Sensors (Basel, Switzerland)
|February 13, 2026
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Resumen

Este estudio presenta el modelo HFHE-Cloud (Heterogeneous Federated Homomorphic Encryption Cloud), que mejora la seguridad y eficiencia del entrenamiento distribuido. HFHE-Cloud logra una alta protección de la privacidad y un rendimiento computacional en entornos de nube multi-inquilino.

Palabras clave:
computación en la nubeentrenamiento distribuidoestudio federalcifrado homomórficoprotección de la privacidad

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Área de la Ciencia:

  • Computación en la Nube
  • Sistemas Distribuidos
  • Criptografía
  • Aprendizaje Automático

Sus antecedentes:

  • El entrenamiento distribuido en entornos de nube enfrenta desafíos como la fuga de privacidad, la redundancia de comunicación y la reducción de la eficiencia de agregación debido a la multi-tenencia y la alta concurrencia.
  • Los modelos de aprendizaje federado existentes luchan por equilibrar la protección de la privacidad con el rendimiento computacional en infraestructuras de nube complejas.

Objetivo del estudio:

  • Desarrollar un marco de aprendizaje colaborativo seguro y eficiente para plataformas en la nube que optimice la protección de la privacidad y el rendimiento computacional.
  • Abordar los cuellos de botella de rendimiento asociados con los cálculos de cifrado y los retrasos de comunicación en el entrenamiento distribuido.

Principales métodos:

  • Se propuso el modelo HFHE-Cloud (Heterogeneous Federated Homomorphic Encryption Cloud), que integra el aprendizaje federado (FL) con el cifrado homomórfico.
  • Se implementó un mecanismo jerárquico de mapeo de claves y programación dinámica para nodos heterogéneos para reducir los cuellos de botella de rendimiento.
  • Se garantizó la privacidad de los datos al no exponer los datos originales durante el proceso de entrenamiento colaborativo.

Principales resultados:

  • HFHE-Cloud superó significativamente a los modelos de referencia (FedAvg, FedProx, FedPer, FedNova, HE-FedAvg) en rendimiento integral.
  • Logró una alta protección de la privacidad con una precisión global de hasta el 94,25 % y una pérdida estable dentro de 0,09.
  • Mejoró el rendimiento computacional al reducir el tiempo de cifrado/descifrado en aproximadamente un tercio, con una sobrecarga de cifrado del 13 %.
  • Mejoró la eficiencia del entrenamiento distribuido al reducir las rondas de comunicación en aproximadamente una quinta parte y mantener una participación de nodos superior al 90 %.

Conclusiones:

  • El modelo HFHE-Cloud demuestra alta seguridad y escalabilidad, adecuado para entornos de nube multi-inquilino.
  • Proporciona una solución técnica viable para proveedores de servicios en la nube y empresas que buscan una sólida protección de la privacidad y un entrenamiento colaborativo eficiente.
  • Equilibra con éxito los requisitos intensivos de privacidad con un rendimiento de aprendizaje distribuido eficiente en plataformas en la nube.