Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
This addresses the challenge of incorporating high-quality private data without sharing it, which is incremental for scaling foundation models.
The paper tackles the problem of data quality control in federated fine-tuning of foundation models by proposing a pipeline that scores training data and sets a global threshold, resulting in improved model performance.
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research. To further scale up, it is crucial to incorporate collaboration among multiple specialized and high-quality private domain data sources. However, the challenge of training models locally without sharing private data presents numerous obstacles in data quality control. To tackle this issue, we propose a data quality control pipeline for federated fine-tuning of foundation models. This pipeline computes scores reflecting the quality of training data and determines a global threshold for a unified standard, aiming for improved global performance. Our experiments show that the proposed quality control pipeline facilitates the effectiveness and reliability of the model training, leading to better performance.