  The advent of Federated Learning has enabled the creation of a
high-performing model as if it had been trained on a considerable amount of
data. A multitude of participants and a server cooperatively train a model
without the need for data disclosure or collection. The healthcare industry,
where security and privacy are paramount, can substantially benefit from this
new learning paradigm, as data collection is no longer feasible due to
stringent data policies. Nonetheless, unaddressed challenges and insufficient
attack mitigation are hampering its adoption. Attack surfaces differ from
traditional centralized learning in that the server and clients communicate
between each round of training. In this paper, we thus present vulnerabilities,
attacks, and defenses based on the widened attack surfaces, as well as suggest
promising new research directions toward a more robust FL.
