Federated Concept-Based Models: Interpretable models with distributed supervision
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911671427006464 |
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| author | Fenoglio, Dario Casanova, Arianna De Santis, Francesco Dominici, Gabriele Schneider, Johannes Barbiero, Pietro De Felice, Giovanni Langheinrich, Marc Gjoreski, Martin |
| author_facet | Fenoglio, Dario Casanova, Arianna De Santis, Francesco Dominici, Gabriele Schneider, Johannes Barbiero, Pietro De Felice, Giovanni Langheinrich, Marc Gjoreski, Martin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04093 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Federated Concept-Based Models: Interpretable models with distributed supervision Fenoglio, Dario Casanova, Arianna De Santis, Francesco Dominici, Gabriele Schneider, Johannes Barbiero, Pietro De Felice, Giovanni Langheinrich, Marc Gjoreski, Martin Machine Learning 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. |
| title | Federated Concept-Based Models: Interpretable models with distributed supervision |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.04093 |