Privacy-Preserving Federated Retrieval-Augmented Generation for Distributed Knowledge Systems
Keywords:
privacy-preserving federated learning; retrieval-augmented generation; distributed knowledge systems; differential privacy; secure aggregation; trustworthy artificial intelligence; socio-technical infrastructure; governanceAbstract
Privacy-preserving federated retrieval-augmented generation has emerged as a critical systems direction for distributed knowledge environments in which organizations and communities need the reasoning power of large generative models without centralizing sensitive documents, proprietary indexes, or user interaction logs. This paper examines the architectural, infrastructural, governance, and policy dimensions of federated retrieval-augmented generation as a socio-technical system rather than a narrow model optimization problem. It argues that the central design challenge is not merely protecting individual model updates, but coordinating heterogeneous retrieval sources, heterogeneous privacy guarantees, heterogeneous institutional rules, and heterogeneous trust boundaries across a distributed knowledge fabric. The discussion analyzes secure aggregation, differential privacy, encrypted retrieval, local indexing, semantic fusion, and distributed evaluation as interdependent components whose trade-offs shape robustness, fairness, sustainability, and accountability. It further considers how governance mechanisms, auditability, model cards, datasheets, and policy constraints must be embedded into the system lifecycle. The paper concludes that privacy-preserving federated retrieval-augmented generation can support more equitable and resilient knowledge systems only if privacy is treated as an infrastructural property, retrieval as a governed common-pool resource, and evaluation as a multi-stakeholder process rather than a centralized benchmark exercise.
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