In this work, we propose GPT-FL, a generative pre-trained model-assisted
federated learning (FL) framework. At its core, GPT-FL leverages generative
pre-trained models to generate diversified synthetic data. These generated data
are used to train a downstream model on the server, which is then fine-tuned
with private client data under the standard FL framework. We show that GPT-FL
consistently outperforms state-of-the-art FL methods in terms of model test
accuracy, communication efficiency, and client sampling efficiency. Through
comprehensive ablation analysis, we discover that the downstream model
generated by synthetic data plays a crucial role in controlling the direction
of gradient diversity during FL training, which enhances convergence speed and
contributes to the notable accuracy boost observed with GPT-FL. Also,
regardless of whether the target data falls within or outside the domain of the
pre-trained generative model, GPT-FL consistently achieves significant
performance gains, surpassing the results obtained by models trained solely
with FL or synthetic data.

本文提出了一个基于生成式预训练模型辅助联邦学习框架的 GPT-FL 方法，在提高通信效率和客户抽样效率的同时，优化梯度多样性来增强模型收敛速度和提高模型测试准确性。