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.

翻译：本文提出GPT-FL，一种生成式预训练模型辅助的联邦学习（FL）框架。其核心思想是利用生成式预训练模型生成多样化的合成数据，这些数据用于在服务器端训练下游模型，随后在标准联邦学习框架下通过客户端私有数据进行微调。实验表明，GPT-FL在模型测试精度、通信效率和客户端采样效率方面均持续优于现有最先进的联邦学习方法。通过全面的消融分析，我们发现由合成数据生成的下游模型在控制联邦学习训练过程中梯度多样性的方向上起到关键作用，这能够提升收敛速度，并显著促进GPT-FL观测到的精度提升。此外，无论目标数据属于生成式预训练模型的领域内还是领域外，GPT-FL始终能取得显著的性能增益，超越仅使用联邦学习或仅使用合成数据训练的模型效果。