Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
Wanru Zhao Yaxin Du Nicholas Donald Lane Siheng Chen Yanfeng Wang
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.