¿Qué es el aprendizaje dividido?
El Aprendizaje Dividido es un en aprendizaje automático that allows multiple parties to collaborate on training a model without sharing their raw data. This method is particularly useful in scenarios where privacidad de datos is a concern, such as in healthcare o finanzas.
In traditional machine learning, a single entity collects and processes all the data to train a model. However, Split Learning changes this paradigm by splitting the arquitectura del modelo and the training process into two distinct parts. One party (often referred to as the client) mantiene las capas iniciales del modelo, mientras que la otra parte (el server) contiene las capas restantes.
During the training process, the client processes its local data through its portion of the model, generating intermediate outputs. These outputs are then sent to the server, which completes the forward pass with its layers and computes the loss function. The server can then send the gradients back to the client for updating its part of the model. This proceso iterativo continúa hasta que el modelo alcanza un nivel aceptable de rendimiento.
Al emplear el aprendizaje dividido, las organizaciones pueden mantener una estricta privacidad de los datos, ya que los datos en bruto nunca salen del lado del cliente. En cambio, solo se intercambian los gradientes y las actualizaciones del modelo, lo que reduce significativamente el riesgo de exponer información sensible.
This approach not only enhances privacy but also allows for more efficient use of recursos computacionales, as it enables the sharing of model training across different devices or locations. Overall, Split Learning is an innovative solution that addresses the challenges of privacy and data security in collaborative machine learning.