Current research and prospects of federated language large models in the medical field

This paper delves into the current state and future prospects of federated learning (FL) in the medical field, with a focus on its key advantages in privacy protection, data efficiency, and model performance improvement. The main innovations of the paper lie in the introduction of three strategies: federated pre-training, fine-tuning, and prompt engineering, aimed at balancing data privacy and model performance optimization. Additionally, the paper discusses the major technical challenges faced by federated learning, including security threats, privacy breaches, and issues with non-independent and identically distributed (Non-IID) data, proposing the use of differential privacy and secure multi-party computation techniques to enhance data privacy and model security. Finally, the paper outlines future research directions, including designing more comprehensive security and privacy protection mechanisms for federated learning, optimizing communication efficiency, and developing more effective algorithms for Non-IID data, all of which are crucial factors in promoting the widespread application of FL in the medical field.