Enhancing Data Quality: Conclusion and Future Work, and References

Table of Links

• Abstract and Introduction
• Motivation and Setup: How low-quality data affects the performance of Collaborative Training
• Proposed Workflow for Data Quality Control
• Experiments
• Conclusion and Future Work, and References
• A. Related Work
• B. Heterogeneity Settings
• C. Experimental Details
• D. Ablation study of Unified Scoring with Anchor Data
• E. Examples for low-and high- quality Data

CONCLUSION AND FUTURE WORK

In this paper, we establish a data quality control pipeline for federated fine-tuning of LLMs, avoiding directly sharing any private data. Preliminary experiments show that the selected high-quality data ensures an effective and reliable learning process, leading to improved model performance. To further safeguard privacy, we can seamlessly integrate differential privacy or a secure aggregation component to prevent reconstruction attacks, enhancing the security of our framework.

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