  Federated learning is an emerging learning paradigm where multiple clients
collaboratively train a machine learning model in a privacy-preserving manner.
Personalized federated learning extends this paradigm to overcome heterogeneity
across clients by learning personalized models. Recently, there have been some
initial attempts to apply Transformers to federated learning. However, the
impacts of federated learning algorithms on self-attention have not yet been
studied. This paper investigates this relationship and reveals that federated
averaging algorithms actually have a negative impact on self-attention where
there is data heterogeneity. These impacts limit the capabilities of the
Transformer model in federated learning settings. Based on this, we propose
FedTP, a novel Transformer-based federated learning framework that learns
personalized self-attention for each client while aggregating the other
parameters among the clients. Instead of using a vanilla personalization
mechanism that maintains personalized self-attention layers of each client
locally, we develop a learn-to-personalize mechanism to further encourage the
cooperation among clients and to increase the scablability and generalization
of FedTP. Specifically, the learn-to-personalize is realized by learning a
hypernetwork on the server that outputs the personalized projection matrices of
self-attention layers to generate client-wise queries, keys and values.
Furthermore, we present the generalization bound for FedTP with the
learn-to-personalize mechanism. Notably, FedTP offers a convenient environment
for performing a range of image and language tasks using the same federated
network architecture - all of which benefit from Transformer personalization.
Extensive experiments verify that FedTP with the learn-to-personalize mechanism
yields state-of-the-art performance in non-IID scenarios. Our code is available
online.
