Federated Concept-Based Models: Interpretable models with distributed supervision

TL;DR

This paper introduces Federated Concept-based Models (F-CMs), enabling interpretable, privacy-preserving, and adaptable concept-based learning across multiple institutions with distributed supervision.

Contribution

F-CMs provide a novel federated learning framework that adapts to evolving concept sets and enables interpretability without requiring shared concept annotations.

Findings

F-CMs maintain accuracy comparable to full supervision.

F-CMs outperform non-adaptive federated baselines.

F-CMs enable interpretability on unseen concepts.

Abstract

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.