ORCHID-FL: On-Device Relationship-Aware Contextual Handling of Information via Decentralized Federated Learning
: Mobile devices store highly sensitive personal data, yet privacy failures often arise not from technical compromise but from mismatches between user intent and sharing interfaces that offer little contextual nuance. We propose ORCHID-FL, an on-device, language-mediated privacy agent that combines tag-grounded perception, a promptable LLM that proposes sharing decisions, and a deterministic rule engine that retains final authority. To allow the agent to adapt to evolving social and institutional norms without centralising sensitive data, ORCHID-FL is designed to participate in a federated fine-tuning loop using parameter-efficient (LoRA-style) updates. We position this paper as an architectural proposal: we present the system design, a hybrid LLM + deterministic-rule decision pipeline, and a threat model that treats the LLM and the federation channel as untrusted. We provide preliminary empirical evidence on the perception layer by comparing CNN-tag, multimodal-tag, and end-to-end multimodal architectures on 200 VizWiz-Priv images annotated by the author, and find that structured tag-grounded pipelines achieve the highest decision agreement and the lowest unintended disclosure rate. Federated fine-tuning evaluation, multi-annotator validation, and quantification of the differential-privacy/utility trade-off are deferred to future work.