Federated Transfer Learning for On-Device LLMs Efficient Fine Tuning Optimization

The proliferation of Large Language Models (LLMs) has catalyzed the growth of various industries. It is therefore imperative to ensure the controlled and beneficial application of LLMs across specific domains for downstream tasks through transfer learning, while preserving their general capabilities. We propose a novel and on-device efficient fine-tuning optimization algorithm for LLMs, utilizing federated transfer learning. Specifically, we introduce the Fusion of low Rank Adaptation (FoRA) optimization algorithm from a micro perspective, which enhances multi-dimensional feature aggregation through the addition of efficient parameters. From a meso perspective, we extend the application of the FoRA algorithm across all linear layers within the Transformer architecture to facilitate downstream task performance. Finally, from a macro perspective and with a focus on the medical domain, we incorporate quantization techniques into the federated learning framework to achieve on-device efficient fine-tuning optimization, thereby offering dual protection for data and model integrity. Our results indicate that, compared to existing state-of-the-art methods, our algorithm significantly improves LLM performance while ensuring dual privacy protection of both data and models.