  Tackling non-IID data is an open challenge in federated learning research.
Existing FL methods, including robust FL and personalized FL, are designed to
improve model performance without consideration of interpreting non-IID across
clients. This paper aims to design a novel FL method to robust and interpret
the non-IID data across clients. Specifically, we interpret each client's
dataset as a mixture of conceptual vectors that each one represents an
interpretable concept to end-users. These conceptual vectors could be
pre-defined or refined in a human-in-the-loop process or be learnt via the
optimization procedure of the federated learning system. In addition to the
interpretability, the clarity of client-specific personalization could also be
applied to enhance the robustness of the training process on FL system. The
effectiveness of the proposed method have been validated on benchmark datasets.
