Towards Privacy-Preserving UAV Agents: A Hybrid Federated Learning - Belief Desire Intention Architecture for Ambient Disaster Response

Autonomous remote robots are increasingly deployed in disaster response scenarios to support critical tasks such as victim localization and damage assessment. However, the ambient nature of such environments, which is marked by uncertainty, data heterogeneity, and limited connectivity, usually poses significant challenges to autonomous decision-making and trust. Thus, this paper proposes a framework for a multi-layered approach for a hybrid agent architecture that integrates Federated Learning (FL) with Belief-Desire-Intention (BDI) models, enabling remote robotic agents to learn collaboratively from distributed data using Distributed Ledger Technology (DLT) while preserving privacy, and to reason about their goals and intentions using cognitive
frameworks of eXplainable AI (XAI). We further present a methodology for coherently embedding FL outcomes into BDI reasoning through semantic mapping and learning-enhanced ontologies. This integration will allow agents to dynamically update their
beliefs and intentions based on learned insights, thereby enhancing autonomy, adaptability, and explainability in ambient disaster response systems.