Personalized Interpretation on Federated Learning: A Virtual Concepts approach
This addresses the problem of interpreting non-IID data across clients in federated learning, which is an incremental improvement over existing FL methods.
The paper tackles the challenge of non-IID data in federated learning by proposing a method that interprets each client's dataset as a mixture of conceptual vectors, which enhances interpretability and robustness. The method was validated on benchmark datasets.
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.