private-data

This paper presents a cross-domain benchmark for federated fine-tuning of large language models on private data, evaluating LoRA, QLoRA, and IA3 strategies on healthcare and finance datasets. Results show federated fine-tuning approaches centralized performance and outperforms isolated learning, supporting its viability for adapting LLMs when data cannot be shared.