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Related Experiment Video

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

A federated learning framework based on transfer learning and knowledge distillation for targeted advertising.

Caiyu Su¹, Jinri Wei¹, Yuan Lei²

¹Guangxi Vocational & Technical Institute of Industry, Nanning, Guangxi, China.

Peerj. Computer Science

|September 14, 2023

Summary

This study introduces a novel framework using C-means clustering and dynamic selection to overcome data privacy issues and model heterogeneity in targeted advertising, improving data sharing efficiency and accuracy.

Keywords:

Federated learning Knowledge distillation Maximum mean difference Targeted advertising Transfer learning

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Area of Science:

Computer Science
Data Science
Artificial Intelligence

Background:

Targeted advertising faces challenges due to privacy data leaks and advertisers' reluctance to share information.
This leads to isolated data islands and model heterogeneity, hindering effective data utilization.

Purpose of the Study:

To develop a secure and efficient data sharing framework for targeted advertising.
To address data island and model heterogeneity challenges in federated learning environments.

Main Methods:

A C-means clustering algorithm based on maximum average difference was proposed to evaluate parameter distribution differences.
An innovative dynamic selection algorithm incorporating knowledge distillation and weight correction was introduced to mitigate model heterogeneity.

Main Results:

The proposed framework demonstrated superior performance compared to existing models.
Evaluated using accuracy, loss, and AUC (area under the ROC curve), the framework achieved higher accuracy, lower loss, and better AUC.
The framework maintained performance efficiency with equivalent computation time.

Conclusions:

The developed framework offers a more reliable, controllable, and secure approach to data sharing.
This enhances the overall efficiency and accuracy of targeted advertising systems.
The study provides a robust solution for privacy-preserving data collaboration in advertising.