  Federated learning is fast becoming a popular paradigm for applications
involving mobile devices, banking systems, healthcare, and IoT systems. Hence,
over the past five years, researchers have undertaken extensive studies on the
privacy leaks, security threats, and fairness associated with these emerging
models. For the most part, these three critical concepts have been studied in
isolation; however, recent research has revealed that there may be an intricate
interplay between them. For instance, some researchers have discovered that
pursuing fairness may compromise privacy, or that efforts to enhance security
can impact fairness. These emerging insights shed light on the fundamental
connections between privacy, security, and fairness within federated learning,
and, by delving deeper into these interconnections, we may be able to
significantly augment research and development across the field. Consequently,
the aim of this survey is to offer comprehensive descriptions of the privacy,
security, and fairness issues in federated learning. Moreover, we analyze the
complex relationships between these three dimensions of cyber safety and
pinpoint the fundamental elements that influence each of them. We contend that
there exists a trade-off between privacy and fairness and between security and
gradient sharing. On this basis, fairness can function as a bridge between
privacy and security to build models that are either more secure or more
private. Building upon our observations, we identify the trade-offs between
privacy and fairness and between security and fairness within the context of
federated learning. The survey then concludes with promising directions for
future research in this vanguard field.
