FLARE: Reliable Local Explanations through Federated Representation Learning

Federated learning enables collaborative model training without sharing raw data, but local explanation methods remain constrained by the data available at individual clients. When local data are sparse, imbalanced, or heterogeneous, query neighborhoods may provide insufficient support for representative and faithful explanations. Although federated explainability methods can exploit distributed information in different ways, the use of collaboratively learned representations to improve real-instance, query-specific neighborhood construction for local surrogate explanations remains largely unexplored. We propose FLARE, a federated framework for reliable local explanations. To the best of our knowledge, FLARE is the first framework to combine federated representation learning with client-local, real-instance neighborhoods for query-specific surrogate explanations. A collaboratively trained SCARF encoder guides local source–target neighborhood construction, while support-aware neighborhood completion and adaptive surrogate weighting improve reliability. Explanations are generated using weighted Ridge surrogates and evaluated for fidelity, support, stability, and robustness. We evaluate FLARE across five tabular datasets spanning seven classification tasks, including predefined source–target domain shifts and natural multi-client partitions, under varying client counts, federated optimizers, and black-box predictors. FLARE achieves the strongest overall fidelity across the evaluated baselines, with sensitivity analyses showing that it remains effective across different client counts and black-box predictors. These results show that cross-client structural knowledge can improve local explanation construction while keeping raw data and explanation generation client-local.