<![CDATA[The proliferation of misinformation in the Indonesian digital ecosystem presents a critical challenge for public discourse, democratic integrity, and social cohesion. Conventional centralized detection systems, while effective, impose significant privacy risks upon contributing institutions including media organizations, universities, and government agencies that possess unique and sensitive corpora. This review investigates the emerging paradigm of Federated Retrieval-Augmented Generation (Federated RAG), which synthesizes Federated Learning (FL) with Retrieval-Augmented Generation to enable privacy-preserving, collaborative misinformation detection across multi-institutional environments. The findings reveal that while Federated RAG represents a nascent yet promising frontier, no prior study has applied this paradigm to Indonesian-language misinformation in a cross-silo institutional setting. This review identifies key technical gaps, proposes a novel architectural taxonomy, and provides a roadmap for future empirical investigations. The framework presented herein is designed to be extensible to other low-resource languages across Southeast Asia and beyond]]>
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