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

TL;DR

This paper develops a comprehensive taxonomy and framework for ensuring trustworthiness in federated learning systems, emphasizing continuous trust management, decision transparency, and system-level challenges in high-stakes, agentic AI environments.

Contribution

It introduces a requirement-driven taxonomy of trust challenges in federated learning and a coordination blueprint for managing trade-offs and governance, operationalized through a privacy-preserving trust evidence artifact.

Findings

Taxonomy clarifies trust challenges in high-risk FL deployments.

Coordination blueprint guides decision-making and governance.

Trust evidence artifact supports assurance without data centralization.

Abstract

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.