Enhancing Data Quality in Federated Fine-Tuning of Foundation Models

• URL: http://arxiv.org/abs/2403.04529v1
• Date: Thu, 7 Mar 2024 14:28:04 GMT
• Title: Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
• Authors: Wanru Zhao, Yaxin Du, Nicholas Donald Lane, Siheng Chen, Yanfeng Wang
• Abstract summary: 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. Our experiments show that the proposed quality control pipeline facilitates the effectiveness and reliability of the model training, leading to better performance.
• Score: 54.757324343062734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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