FedPerfix: Towards Partial Model Personalization of Vision Transformers
  in Federated Learning
Guangyu Sun Matias Mendieta Jun Luo Shandong Wu Chen Chen
Personalized Federated Learning (PFL) represents a promising solution for
decentralized learning in heterogeneous data environments. Partial model
personalization has been proposed to improve the efficiency of PFL by
selectively updating local model parameters instead of aggregating all of them.
However, previous work on partial model personalization has mainly focused on
Convolutional Neural Networks (CNNs), leaving a gap in understanding how it can
be applied to other popular models such as Vision Transformers (ViTs). In this
work, we investigate where and how to partially personalize a ViT model.
Specifically, we empirically evaluate the sensitivity to data distribution of
each type of layer. Based on the insights that the self-attention layer and the
classification head are the most sensitive parts of a ViT, we propose a novel
approach called FedPerfix, which leverages plugins to transfer information from
the aggregated model to the local client as a personalization. Finally, we
evaluate the proposed approach on CIFAR-100, OrganAMNIST, and Office-Home
datasets and demonstrate its effectiveness in improving the model's performance
compared to several advanced PFL methods.