Federated Explainability

A decentralized implementation of Shapley additive explanations that computes feature attribution scores across distributed data partitions. Each client calculates local SHAP values on their private dataset, and a central server securely aggregates these contributions to produce a global explanation model.

• Preserves the efficiency, symmetry, and additivity axioms of classical SHAP
• Uses secure aggregation protocols to prevent leakage of individual patient-level attributions
• Enables identification of which clinical features drive predictions across the entire federated network
• Critical for auditing whether a diagnostic model relies on clinically valid biomarkers rather than spurious correlations

A federated adaptation of Local Interpretable Model-agnostic Explanations that generates locally faithful explanations for individual predictions without accessing raw data from other institutions. Each client trains a local surrogate model on perturbed samples of their own data.

• Produces instance-level explanations showing which features influenced a specific patient's prediction
• Surrogate models are typically sparse linear models or decision trees for high interpretability
• Aggregation of local explanation fidelity metrics across clients identifies globally important features
• Particularly useful for clinician-facing decision support where individual prediction rationale is required

A class of techniques for computing global feature importance scores across decentralized datasets by aggregating permutation importance or integrated gradients from each participating institution. The central server combines these scores without accessing underlying feature values.

• Permutation importance measures performance degradation when a feature is randomly shuffled
• Integrated gradients compute the path integral of gradients from a baseline to the actual input
• Aggregation uses weighted averaging proportional to each client's dataset size
• Enables regulatory compliance by demonstrating which variables the model actually uses for decision-making

A privacy-preserving method for generating actionable what-if scenarios that show how a patient's features would need to change to alter a model's prediction. Each client generates counterfactuals on their local data, and the system aggregates plausible intervention pathways.

• Answers questions like: "What change in lab values would flip this diagnosis?"
• Enforces clinical plausibility constraints to ensure generated counterfactuals are medically feasible
• Uses federated optimization to find minimal feature perturbations across the distributed population
• Supports shared decision-making between clinicians and patients by illustrating modifiable risk factors

A decentralized technique for visualizing the marginal effect of one or two features on a model's predictions while averaging out the effects of all other features. Each client computes partial dependence on their local data partition, and results are securely aggregated.

• Reveals non-linear relationships between clinical variables and predicted outcomes
• Identifies threshold effects where risk changes dramatically at specific feature values
• Aggregation preserves the shape of the functional relationship without exposing individual data points
• Essential for clinical validation to ensure the model's learned relationships align with established medical knowledge

An advanced interpretability approach that explains model decisions in terms of high-level clinical concepts rather than raw input features. Concepts like "tumor heterogeneity" or "cardiac remodeling" are defined locally, and their influence is aggregated across the federated network.

• Uses concept activation vectors to measure how sensitive predictions are to clinically meaningful abstractions
• Concepts are defined by each institution using their own expert-annotated exemplars
• Aggregation reveals cross-institutional consensus on which clinical concepts drive predictions
• Aligns model explanations with the diagnostic reasoning language that clinicians actually use

A decentralized implementation of Shapley additive explanations that computes feature importance scores without centralizing patient data. Each institution calculates local Shapley values on its own data partition, then a secure aggregation protocol combines these contributions into global feature attributions. This preserves the efficiency, symmetry, and additivity properties of classical SHAP while maintaining HIPAA compliance. The approach enables clinicians to understand which biomarkers, imaging features, or demographic factors drove a specific prediction across the entire federated cohort.

A federated adaptation of Local Interpretable Model-agnostic Explanations that generates locally faithful explanations for individual predictions. The process works by:

• Sampling perturbed instances around a prediction of interest
• Querying the federated model for predictions on these perturbations
• Fitting an interpretable surrogate model like linear regression or decision trees
• Extracting feature weights as the explanation

Because the surrogate model is trained on synthetic perturbations rather than real patient data, no protected health information is exposed during the explanation generation process.

A systematic framework for verifying that a federated model's decision-making aligns with established clinical knowledge and regulatory requirements. This involves:

• Global feature importance computed across all participating institutions
• Feature stability analysis to detect when attributions diverge between sites
• Protected attribute monitoring to ensure sensitive demographics are not inappropriately influencing predictions
• Temporal drift detection in feature importance patterns

Regulatory bodies can use these audit trails to validate that models comply with FDA SaMD guidelines and EU AI Act transparency mandates.

A technique that generates actionable what-if scenarios to explain model decisions in a privacy-preserving manner. For a patient denied a treatment recommendation, the system computes the minimal set of feature changes that would flip the prediction to a positive outcome. These counterfactuals are generated using gradient-based optimization or genetic algorithms operating on the federated model's decision boundary, without accessing any individual's training data. The resulting explanations provide clinicians with concrete clinical intervention pathways.

A gradient-based attribution method adapted for federated settings that satisfies the axioms of sensitivity and implementation invariance. The technique computes feature importance by:

• Interpolating between a baseline input and the actual input
• Accumulating gradients along this path
• Aggregating gradient contributions across all federated clients

This method is particularly effective for deep neural networks used in medical imaging and genomic analysis, providing pixel-level or nucleotide-level explanations that radiologists and geneticists can visually validate against their domain expertise.

A centralized visualization interface that aggregates and displays model interpretations from all participating institutions without exposing raw data. Key components include:

• Global feature importance rankings with confidence intervals
• Institution-level explanation drift monitoring
• Subgroup fairness metrics stratified by demographics
• Real-time SHAP waterfall plots for individual predictions
• Audit logs tracking all explanation queries for compliance

These dashboards serve as the primary interface for AI governance boards and clinical review committees to oversee federated model behavior.