[2507.15796] From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0

Computer Science > Artificial Intelligence arXiv:2507.15796 (cs) [Submitted on 21 Jul 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0 Authors:Nuria Rodríguez-Barroso, Mario García-Márquez, M. Victoria Luzón, Francisco Herrera View a PDF of the paper titled From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0, by Nuria Rodr\'iguez-Barroso and Mario Garc\'ia-M\'arquez and M. Victoria Luz\'on and Francisco Herrera View PDF HTML (experimental) 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. ...