  Federated Learning (FL) is a concept first introduced by Google in 2016, in
which multiple devices collaboratively learn a machine learning model without
sharing their private data under the supervision of a central server. This
offers ample opportunities in critical domains such as healthcare, finance etc,
where it is risky to share private user information to other organisations or
devices. While FL appears to be a promising Machine Learning (ML) technique to
keep the local data private, it is also vulnerable to attacks like other ML
models. Given the growing interest in the FL domain, this report discusses the
opportunities and challenges in federated learning.
