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Authors:
(1) Wanru Zhao, University of Cambridge, Shanghai AI Laboratory with Equal contribution;
(2) Yaxin Du, Shanghai Jiao Tong University with Equal contribution;
(3) Nicholas D. Lane, University of Cambridge and Flower Labs;
(4) Siheng Chen, Shanghai AI Laboratory and Shanghai Jiao Tong University;
(5) Yanfeng Wang, Shanghai AI Laboratory and Shanghai Jiao Tong University.
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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This paper is available on arxiv under CC BY 4.0 DEED license.