  Federated learning, as a privacy-preserving collaborative machine learning
paradigm, has been gaining more and more attention in the industry. With the
huge rise in demand, there have been many federated learning platforms that
allow federated participants to set up and build a federated model from
scratch. However, exiting platforms are highly intrusive, complicated, and hard
to integrate with built machine learning models. For many real-world businesses
that already have mature serving models, existing federated learning platforms
have high entry barriers and development costs. This paper presents a simple
yet practical federated learning plug-in inspired by ensemble learning, dubbed
WrapperFL, allowing participants to build/join a federated system with existing
models at minimal costs. The WrapperFL works in a plug-and-play way by simply
attaching to the input and output interfaces of an existing model, without the
need of re-development, significantly reducing the overhead of manpower and
resources. We verify our proposed method on diverse tasks under heterogeneous
data distributions and heterogeneous models. The experimental results
demonstrate that WrapperFL can be successfully applied to a wide range of
applications under practical settings and improves the local model with
federated learning at a low cost.
