This research investigates federated large language models and trustworthy retrieval-augmented generation for aviation, transportation, industrial, and low-altitude operation scenarios where data and professional knowledge are distributed across different organizations. The objective is to enable collaborative optimization of retrievers, generators, and knowledge representations without centralizing raw data or local knowledge bases. Major topics include evidence package construction, provenance verification, knowledge freshness assessment, conflicting evidence detection, trustworthiness scoring, poisoned knowledge defense, and generation traceability. The research aims to make generated answers verifiable, explainable, traceable, and auditable.