Harnessing Federated Learning for LLM Fine-Tuning: A Distributed Approach

Sammanfattning

Large Language Models (LLMs) have emerged as a game-changing force in computerizedlanguage processing. These models have an astonishing ability to understand complex linguisticpatterns and generate responses that are both coherent and contextually appropriate. LLMs, asubset of Artificial Intelligence (AI), are used for a variety of tasks, including Natural LanguageProcessing (NLP), machine translation, and question answering. While they excel at simulatinghuman-like text, these models face significant challenges. To learn effectively, these models require significant computational resources and large amountsof data. Unfortunately, many organizations struggle to gain access to such computing power.Furthermore, privacy laws can constrain the acquisition of sensitive data, limiting the growth ofthese technologies. This paper suggests fine-tuning techniques that reduce computationaldemands by adjusting pre-trained models to specific tasks. We used the Low-Rank Adaptation(LoRA) technique, which modifies only a small fraction of model parameters while maintaininghigh performance. In addition, we integrate federated learning approaches using the Fedn framework to addressprivacy concerns, distributing the training process across multiple devices while keeping datalocalized. Finally, We conducted a series of experiments on the T5 model, applying these methods to asummarization task in both centralized and federated settings. Our findings reveal that our methods achieve two critical goals,reducing computational costs andensuring data privacy. These results have significant implications for the future development ofLLMs. By adopting our approaches, LLMs training can become more efficient and privacyconscious.