  Deep learning models have shown their advantage in many different tasks,
including neuroimage analysis. However, to effectively train a high-quality
deep learning model, the aggregation of a significant amount of patient
information is required. The time and cost for acquisition and annotation in
assembling, for example, large fMRI datasets make it difficult to acquire large
numbers at a single site. However, due to the need to protect the privacy of
patient data, it is hard to assemble a central database from multiple
institutions. Federated learning allows for population-level models to be
trained without centralizing entities' data by transmitting the global model to
local entities, training the model locally, and then averaging the gradients or
weights in the global model. However, some studies suggest that private
information can be recovered from the model gradients or weights. In this work,
we address the problem of multi-site fMRI classification with a
privacy-preserving strategy. To solve the problem, we propose a federated
learning approach, where a decentralized iterative optimization algorithm is
implemented and shared local model weights are altered by a randomization
mechanism. Considering the systemic differences of fMRI distributions from
different sites, we further propose two domain adaptation methods in this
federated learning formulation. We investigate various practical aspects of
federated model optimization and compare federated learning with alternative
training strategies. Overall, our results demonstrate that it is promising to
utilize multi-site data without data sharing to boost neuroimage analysis
performance and find reliable disease-related biomarkers. Our proposed pipeline
can be generalized to other privacy-sensitive medical data analysis problems.
