  Federated learning (FL) facilitates multiple clients to jointly train a
machine learning model without sharing their private data. However, Non-IID
data of clients presents a tough challenge for FL. Existing personalized FL
approaches rely heavily on the default treatment of one complete model as a
basic unit and ignore the significance of different layers on Non-IID data of
clients. In this work, we propose a new framework, federated model components
self-attention (FedMCSA), to handle Non-IID data in FL, which employs model
components self-attention mechanism to granularly promote cooperation between
different clients. This mechanism facilitates collaboration between similar
model components while reducing interference between model components with
large differences. We conduct extensive experiments to demonstrate that FedMCSA
outperforms the previous methods on four benchmark datasets. Furthermore, we
empirically show the effectiveness of the model components self-attention
mechanism, which is complementary to existing personalized FL and can
significantly improve the performance of FL.
