  Health management has become a primary problem as new kinds of diseases and
complex symptoms are introduced to a rapidly growing modern society. Building a
better and smarter healthcare infrastructure is one of the ultimate goals of a
smart city. To the best of our knowledge, neural network models are already
employed to assist healthcare professionals in achieving this goal. Typically,
training a neural network requires a rich amount of data but heterogeneous and
vulnerable properties of clinical data introduce a challenge for the
traditional centralized network. Moreover, adding new inputs to a medical
database requires re-training an existing model from scratch. To tackle these
challenges, we proposed a deep learning-based clinical decision support system
trained and managed under a federated learning paradigm. We focused on a novel
strategy to guarantee the safety of patient privacy and overcome the risk of
cyberattacks while enabling large-scale clinical data mining. As a result, we
can leverage rich clinical data for training each local neural network without
the need for exchanging the confidential data of patients. Moreover, we
implemented the proposed scheme as a sequence-to-sequence model architecture
integrating the attention mechanism. Thus, our objective is to provide a
personalized clinical decision support system with evolvable characteristics
that can deliver accurate solutions and assist healthcare professionals in
medical diagnosing.
