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

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.

Already published in Information Fusion

References in corpus (13)

• A Survey of Human-in-the-loop for Machine Learning
• Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
• AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
• Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
• Federated learning: Applications, challenges and future directions
• Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
• Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations
• Federated Learning for Cyber Physical Systems: A Comprehensive Survey
• Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
• Distributed Learning of Generalized Linear Causal Networks
• Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions
• TDML -- A Trustworthy Distributed Machine Learning Framework
• Causal Influence in Federated Edge Inference