  Access to sufficient annotated data is a common challenge in training deep
neural networks on medical images. As annotating data is expensive and
time-consuming, it is difficult for an individual medical center to reach large
enough sample sizes to build their own, personalized models. As an alternative,
data from all centers could be pooled to train a centralized model that
everyone can use. However, such a strategy is often infeasible due to the
privacy-sensitive nature of medical data. Recently, federated learning (FL) has
been introduced to collaboratively learn a shared prediction model across
centers without the need for sharing data. In FL, clients are locally training
models on site-specific datasets for a few epochs and then sharing their model
weights with a central server, which orchestrates the overall training process.
Importantly, the sharing of models does not compromise patient privacy. A
disadvantage of FL is the dependence on a central server, which requires all
clients to agree on one trusted central body, and whose failure would disrupt
the training process of all clients. In this paper, we introduce BrainTorrent,
a new FL framework without a central server, particularly targeted towards
medical applications. BrainTorrent presents a highly dynamic peer-to-peer
environment, where all centers directly interact with each other without
depending on a central body. We demonstrate the overall effectiveness of FL for
the challenging task of whole brain segmentation and observe that the proposed
server-less BrainTorrent approach does not only outperform the traditional
server-based one but reaches a similar performance to a model trained on pooled
data.
