  Since the federated learning, which makes AI learning possible without moving
local data around, was introduced by google in 2017 it has been actively
studied particularly in the field of medicine. In fact, the idea of machine
learning in AI without collecting data from local clients is very attractive
because data remain in local sites. However, federated learning techniques
still have various open issues due to its own characteristics such as non
identical distribution, client participation management, and vulnerable
environments. In this presentation, the current issues to make federated
learning flawlessly useful in the real world will be briefly overviewed. They
are related to data/system heterogeneity, client management, traceability, and
security. Also, we introduce the modularized federated learning framework, we
currently develop, to experiment various techniques and protocols to find
solutions for aforementioned issues. The framework will be open to public after
development completes.
