  Motivated by the advancing computational capacity of wireless end-user
equipment (UE), as well as the increasing concerns about sharing private data,
a new machine learning (ML) paradigm has emerged, namely federated learning
(FL). Specifically, FL allows a decoupling of data provision at UEs and ML
model aggregation at a central unit. By training model locally, FL is capable
of avoiding data leakage from the UEs, thereby preserving privacy and security
to some extend. However, even if raw data are not disclosed from UEs,
individual's private information can still be extracted by some recently
discovered attacks in the FL architecture. In this work, we analyze the privacy
and security issues in FL, and raise several challenges on preserving privacy
and security when designing FL systems. In addition, we provide extensive
simulation results to illustrate the discussed issues and possible solutions.
