Comprehensive and Reliable Feature Attribution for Diverse Modalities and Models via Frequency-Domain Insights

Researchers introduce FreqX, a novel interpretability method for machine learning models that leverages signal processing and information theory to address challenges in personalized federated learning. The approach achieves 10x faster performance than existing methods while providing both attribution and concept information while maintaining privacy.

FreqX represents a meaningful advancement in making machine learning models more interpretable, particularly within federated learning environments where privacy and computational efficiency are paramount constraints. The research tackles a fundamental tension in modern AI: the need to understand model decisions without compromising data privacy or computational resources. Personalized federated learning has emerged as a critical framework for collaborative model training across distributed devices, but opacity in model decision-making has hindered adoption in sensitive domains. This interpretability gap becomes especially acute when assessing client contributions and ensuring fairness in federated systems. The introduction of frequency-domain analysis through signal processing provides a novel analytical lens that differs from gradient-based and perturbation-based attribution methods. By processing information in the frequency domain, FreqX can extract both feature importance and higher-level concept representations more efficiently. The 10x speed improvement over concept-aware baselines addresses a practical barrier to deployment, making interpretability accessible for resource-constrained edge devices commonly found in federated networks. For the AI/ML community, this development signals growing momentum toward privacy-preserving interpretability solutions. The dual output of attribution and concept information proves valuable for different stakeholders—developers need attribution for debugging, while regulators and users benefit from concept-level explanations. The work bridges signal processing and deep learning, suggesting that cross-disciplinary approaches can unlock efficiency gains unavailable to traditional ML methods. Future adoption depends on validation across diverse modalities and comparison against emerging explainability frameworks in production federated systems.

• →FreqX achieves 10x faster performance than existing interpretability methods by leveraging frequency-domain analysis
• →The method addresses critical federated learning challenges including privacy preservation and contribution clarity
• →Dual output of attribution and concept information serves both technical and regulatory stakeholder needs
• →Signal processing integration offers a novel analytical approach distinct from gradient and perturbation-based methods
• →Computational efficiency gains make interpretability practical for resource-constrained edge devices