让跨机构模型在不共享数据的前提下，自动适应概念变化并保持可解释性。
Federated Concept-Based Models: Interpretable models with distributed supervision
- 通过联邦学习聚合多机构概念信息，动态调整模型架构。
- 在概念缺失情况下仍能实现与全监督相当的准确率和可解释性。
- 适合医疗、金融等需隐私保护且概念持续演化的场景。
基于概念的模型（CMs）通过将预测结果与人类可理解的概念关联，提升了深度学习的可解释性。然而，概念标注成本高，在单一数据源中难以大规模获取。联邦学习（FL）可通过跨机构协作训练，缓解这一问题，但现有联邦学习缺乏可解释建模范式。将CMs与FL结合具有挑战：尽管FL支持异构、非平稳的客户端参与，但通常假设共享固定架构，而CMs可能需随可用概念集的变化调整架构。本文提出联邦概念模型（F-CMs），一种在动态联邦设置中部署CMs的新方法。F-CMs可在机构间聚合概念级信息，并在概念监督变化时高效适应模型架构，同时保护隐私。实验表明，F-CMs在准确性与干预有效性上与全监督训练相当，平均优于非自适应联邦基线。尤为关键的是，F-CMs可对某机构未掌握的概念实现可解释推理，这是现有方法的显著优势。
原文摘要 · Abstract (English)
Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept annotations are costly and rarely available at scale within a single data source. Federated Learning (FL) could alleviate this limitation by enabling cross-institutional training over concept annotations distributed across multiple data owners. Yet, FL lacks interpretable modeling paradigms. Integrating CMs with FL is non-trivial: although FL supports heterogeneous and non-stationary client participation, it typically assumes a fixed shared architecture, whereas CMs may require architectural adaptation as the available concept set evolves. We propose Federated Concept-based Models (F-CMs), a new methodology for deploying CMs in evolving FL settings. F-CMs aggregate concept-level information across institutions and efficiently adapt the model architecture to changes in concept supervision while preserving privacy. Empirically, F-CMs maintain accuracy and intervention effectiveness comparable to training settings with full concept supervision, while outperforming on average non-adaptive federated baselines. Notably, F-CMs enable interpretable inference on concepts unavailable to a given institution, a key novelty over existing approaches.
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