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TRIP: Mezcla de prompts a nivel de token con enrutamiento sin parámetros para la generalización de dominios federados

Shuai Gong, Chaoran Cui, Xiaolin Dong

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|January 16, 2026

Resumen

TRIP mejora la generalización de dominios mediante el uso de mezclas de prompts a nivel de token y enrutamiento sin parámetros para modelos eficientes y especializados. Este enfoque mejora el rendimiento en datos diversos, superando a los métodos anteriores.

Área de la Ciencia:

Inteligencia Artificial
Aprendizaje Automático
Visión por Computadora

Sus antecedentes:

La Generalización de Dominios Federados (FedDG) entrena modelos en datos descentralizados y heterogéneos.
Los métodos actuales de aprendizaje de prompts para FedDG luchan con la diversidad de muestras, lo que lleva a una degradación del rendimiento.
Las arquitecturas de Mezcla de Expertos (MoE) ofrecen especialización pero enfrentan desafíos en la asignación de expertos y los costos de comunicación.

Objetivo del estudio:

Introducir TRIP, un framework novedoso para FedDG que aborda las limitaciones del aprendizaje de prompts actual basado en MoE.
Habilitar la captura de patrones visuales de grano fino a través de la asignación de expertos a nivel de token.
Reducir la sobrecarga de comunicación a través del enrutamiento sin parámetros.

Principales métodos:

TRIP emplea un framework de Mezcla de Prompts a nivel de Token con enrutamiento sin parámetros.
Los tokens de imágenes individuales se asignan a expertos de prompts distintos para un aprendizaje especializado.
El enrutamiento sin parámetros utiliza agrupación consciente de la capacidad y Transporte Óptimo (OT) para una asignación eficiente de expertos.

Principales resultados:

TRIP logra un rendimiento de generalización óptimo en cuatro benchmarks.
El framework reduce significativamente los costos de comunicación, requiriendo tan solo 1K parámetros.
La asignación a nivel de token captura patrones visuales de grano fino de manera más efectiva que los métodos a nivel de imagen.

Conclusiones:

TRIP ofrece una solución efectiva y eficiente para la Generalización de Dominios Federados.
El mecanismo de enrutamiento sin parámetros propuesto reduce drásticamente la sobrecarga de comunicación.
TRIP demuestra el potencial de la asignación de expertos a nivel de token para modelos especializados y generalizables.

Palabras clave:

Generalización de dominios federados Aprendizaje de prompts Mezcla de expertos Enrutamiento sin parámetros Visión por computadora

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