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arXiv · 2406.19631

Personalized Interpretation on Federated Learning: A Virtual Concepts approach

Peng Yan·Guodong Long·Jing Jiang·Michael Blumenstein
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Abstract

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.

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Peng Yan, Guodong Long, Jing Jiang, Michael Blumenstein. 2024-06-28. Personalized Interpretation on Federated Learning: A Virtual Concepts approach. https://arxiv.org/abs/2406.19631

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