  For early breast cancer detection, regular screening with mammography imaging
is recommended. Routinary examinations result in datasets with a predominant
amount of negative samples. A potential solution to such class-imbalance is
joining forces across multiple institutions. Developing a collaborative
computer-aided diagnosis system is challenging in different ways. Patient
privacy and regulations need to be carefully respected. Data across
institutions may be acquired from different devices or imaging protocols,
leading to heterogeneous non-IID data. Also, for learning-based methods, new
optimization strategies working on distributed data are required. Recently,
federated learning has emerged as an effective tool for collaborative learning.
In this setting, local models perform computation on their private data to
update the global model. The order and the frequency of local updates influence
the final global model. Hence, the order in which samples are locally presented
to the optimizers plays an important role. In this work, we define a
memory-aware curriculum learning method for the federated setting. Our
curriculum controls the order of the training samples paying special attention
to those that are forgotten after the deployment of the global model. Our
approach is combined with unsupervised domain adaptation to deal with domain
shift while preserving data privacy. We evaluate our method with three clinical
datasets from different vendors. Our results verify the effectiveness of
federated adversarial learning for the multi-site breast cancer classification.
Moreover, we show that our proposed memory-aware curriculum method is
beneficial to further improve classification performance. Our code is publicly
available at: https://github.com/ameliajimenez/curriculum-federated-learning.
