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

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.