From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0

Researchers propose Trustworthy Federated Learning (TFL) framework that treats trust as a continuously maintained system condition rather than static property, addressing challenges in AI systems with autonomous decision-making. The framework introduces Trust Report 2.0 as a privacy-preserving coordination blueprint for multi-stakeholder governance in federated learning deployments.

• →Privacy guarantees alone are insufficient for trust in high-risk federated learning deployments, especially with agentic AI systems.
• →Trustworthy Federated Learning (TFL) treats trust as a dynamic operating condition requiring continuous maintenance across system lifecycle.
• →Trust Report 2.0 provides a lightweight, privacy-preserving framework for decision-centric trust evidence without centralizing raw data.
• →The framework addresses autonomous decision-making, non-stationary environments, and multi-stakeholder governance challenges in modern FL systems.
• →Healthcare and oncology applications serve as stress-test domains for the framework under regulatory and clinical risk pressures.