Federated Learning (FL) is an emerging paradigm that enables distributed users to collaboratively and iteratively train machine learning models without sharing their private data. Motivated by the effectiveness and robustness of self-attention-based architectures, researchers are turning to using pre-trained Transformers (i.e., foundation models) instead of traditional convolutional neural networks in FL to leverage their excellent transfer learning capabilities. Despite recent progress, how pre-trained Transformer models play a role in FL remains obscure, that is, how to efficiently fine-tune these pre-trained models in FL and how FL users could benefit from this new paradigm. In this paper, we explore this issue and demonstrate that the fine-tuned Transformers achieve extraordinary performance on FL, and that the lightweight fine-tuning method facilitates a fast convergence rate and low communication costs. Concretely, we conduct a rigorous empirical study of three tuning methods (i.e., modifying the input, adding extra modules, and adjusting the backbone) using two types of pre-trained models (i.e., vision-language models and vision models) for FL. Our experiments show that 1) Fine-tuning the bias term of the backbone performs best when relying on a strong pre-trained model; 2) The vision-language model (e.g., CLIP) outperforms the pure vision model (e.g., ViT) and is more robust to the few-shot settings; 3) Compared to pure local training, FL with pre-trained models has a higher accuracy because it alleviates the problem of over-fitting. We will release our code and encourage further exploration of pre-trained Transformers and FL.

本文研究了如何有效地在联邦学习中使用预训练Transformer模型及其微调方法，实验结果表明，微调模型的偏置项是最好的策略，并且使用视觉-语言模型会比纯视觉模型的性能更好，且能提高模型精度并减少过拟合问题。

FedTune：使用预训练Transformer进行高效联邦微调的深入探究