From privacy to trust in the agentic era: a taxonomy of challenges in trustworthy federated learning through the lens of trust report 2.0

• Trustworthy AI is a prerequisite for deploying FL in high-risk, multi-stakeholder domains. • Privacy-preserving guarantees alone are insufficient to sustain trust in agentic and dynamic FL systems. • We present a requirement-driven taxonomy of challenges aligned with Trustworthy AI principles, explicitly extended to agentic AI and LLM-enabled settings. • We present a coordination blueprint maps Trustworthy AI requirements to FL-native controls and decision-centric, auditable evidence across the lifecycle. • We reframe trust in FL as a continuously maintained operating condition and introduce coordination tools to operationalize it. • We propose Trust Report 2.0 to operationalize trust as evidence, not assumptions, by surfacing what decisions were made, why, and under which constraints, without centralizing raw data. Federated Learning (FL) enables privacy-preserving collaborative learning, yet deployments increasingly show that privacy guarantees alone do not sustain trust in high-risk settings. As FL systems move toward agentic AI, large language model–enabled, and dynamically adaptive architectures, trustworthiness becomes a system-level problem shaped by autonomous decision-making, non-stationary environments, and multi-stakeholder governance. We argue for Trustworthy FL (TFL), treating trust as a continuously maintained operating condition rather than a static model property. Through the lens of Trust Report 2.0, we propose a requirement-driven taxonomy of challenges grounded in TAI and explicitly extended to account for control-plane decisions, agency, and system dynamics across the federated lifecycle. Building on this diagnosis, we introduce a coordination blueprint that structures cross-requirement trade-offs, decision justification, and governance alignment in TFL systems. To operationalize assurance, Trust Report 2.0 is instantiated as a lightweight, privacy-preserving artifact that surfaces decision-centric trust evidence without centralizing raw data. We illustrate applicability via healthcare as a stress-test domain, focusing on oncology FL under regulatory pressure and clinical risk.