Feb. 13, 2024, 5:42 a.m. | Rui Ye Wenhao Wang Jingyi Chai Dihan Li Zexi Li Yinda Xu Yaxin Du Yanfeng Wang Siheng

cs.LG updates on arXiv.org arxiv.org

Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high-quality public data will be exhausted in a few years. In this paper, we offer a potential next step for contemporary LLMs: collaborative and privacy-preserving LLM training on the underutilized distributed private data via federated learning (FL), where multiple data owners collaboratively train a shared model without transmitting raw data. To …

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