Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations

May 27, 2026Open Access

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Key Points

This research aims to enhance explainable AI (XAI) in federated learning while ensuring user privacy.
Developed an open-source framework for XAI in the context of federated learning.
Implemented privacy-preserving techniques to secure user data during AI model training and explanation generation.
Evaluated the framework for performance and security in various scenarios.
Demonstrated the effectiveness of the open-source framework in providing transparent AI explanations while maintaining user privacy.
Showed significant improvements in data privacy metrics compared to traditional approaches.

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Research article explores open source solutions for privacy-preserving explanations in federated learning, suggesting new frameworks for secure AI.

Research article: Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations

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Ivchenko et al. (2026) studied this question.

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