FCDC: Federated Contextual Disclosure Control for Heterogeneous LLM Agents

Contextual integrity (CI) requires large language model agents to disclose information according to the norms of each interaction. Learning this capability collaboratively is difficult when clients differ both in their disclosure requirements and in the main agents that generate candidate actions. We introduce Federated Contextual Disclosure Control (FCDC), integrating federated CI learning with nested local norm adaptation and shared representation learning. A policy-conditioned contextual encoder provides the common parameter space, while client-specific norm decision modules retain local decision states. A nested objective evaluates shared representations through locally adapted decisions; alternating updates couple local norm adaptation to encoder learning, and only encoder parameters are aggregated. This design supports collaboration across frozen, heterogeneous main agents while keeping interaction records and decision parameters local. Evaluation covers nine clients per fold, two held-out-backbone folds without target adaptation, and action-level and closed-loop privacy–utility outcomes. The tabulated held-out balanced-accuracy gains are approximately 11 percentage points over independent training and 2.6–4.7 points over the strongest federated comparator in each fold. In the MAGPIE-derived comparison with the held-out main agent, FCDC has 5.0 percentage points lower focal private-item leakage and 13.3 points higher constraint satisfaction than an adapted CI supervisor.