  Deep learning techniques have been widely used in computed tomography (CT)
but require large data sets to train networks. Moreover, data sharing among
multiple institutions is limited due to data privacy constraints, which hinders
the development of high-performance DL-based CT imaging models from
multi-institutional collaborations. Federated learning (FL) strategy is an
alternative way to train the models without centralizing data from
multi-institutions. In this work, we propose a novel peer-to-peer federated
continual learning strategy to improve low-dose CT imaging performance from
multiple institutions. The newly proposed method is called peer-to-peer
continual FL with intermediate controllers, i.e., icP2P-FL. Specifically,
different from the conventional FL model, the proposed icP2P-FL does not
require a central server that coordinates training information for a global
model. In the proposed icP2P-FL method, the peer-to-peer federated continual
learning is introduced wherein the DL-based model is continually trained one
client after another via model transferring and inter institutional parameter
sharing due to the common characteristics of CT data among the clients.
Furthermore, an intermediate controller is developed to make the overall
training more flexible. Numerous experiments were conducted on the AAPM
low-dose CT Grand Challenge dataset and local datasets, and the experimental
results showed that the proposed icP2P-FL method outperforms the other
comparative methods both qualitatively and quantitatively, and reaches an
accuracy similar to a model trained with pooling data from all the
institutions.
