Personalized Interpretation on Federated Learning: A Virtual Concepts approach
<|reference_start|>Personalized Interpretation on Federated Learning: A Virtual Concepts approach: 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.<|reference_end|>
@article{yan2024personalized,
    title={Personalized Interpretation on Federated Learning: A Virtual Concepts
  approach},
    author={Peng Yan, Guodong Long, Jing Jiang, Michael Blumenstein},
    journal={arXiv preprint arXiv:2406.19631},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19631},
    primaryClass={cs.LG cs.DC}
}