  Federated Learning (FL) is a privacy-preserving distributed learning approach
that is rapidly developing in an era where privacy protection is increasingly
valued. It is this rapid development trend, along with the continuous emergence
of new demands for FL in the real world, that prompts us to focus on a very
important problem: Federated Learning with New Knowledge. The primary challenge
here is to effectively incorporate various new knowledge into existing FL
systems and evolve these systems to reduce costs, extend their lifespan, and
facilitate sustainable development. In this paper, we systematically define the
main sources of new knowledge in FL, including new features, tasks, models, and
algorithms. For each source, we thoroughly analyze and discuss how to
incorporate new knowledge into existing FL systems and examine the impact of
the form and timing of new knowledge arrival on the incorporation process.
Furthermore, we comprehensively discuss the potential future directions for FL
with new knowledge, considering a variety of factors such as scenario setups,
efficiency, and security. There is also a continuously updating repository for
this topic: https://github.com/conditionWang/FLNK.
