You frame this for edge environments, yet the system must transmit sensitive logs to an external API (GPT-4o) for explanations. Doesnt this directly contradict the core edge computing requirements of data security and low latency?
This concern is valid, and IoT-XPLAIN addresses it through architectural separation and deployment flexibility rather than mandatory cloud dependence. In our design, the Random Forest–based detection runs entirely at the edge, ensuring that sensitive data never leaves the device for real-time fault detection. The LLM explanation layer is optional and can be deployed in three modes: offloaded to a trusted private gateway, executed using an on-premise or self-hosted LLM, or anonymized and minimized before transmission to an external API. Only structured, reduced feature representations and model outputs are shared, not raw traffic logs, which mitigates data exposure. As a result, IoT-XPLAIN preserves edge computing principles for latency and security while enabling explainability as an add-on rather than a hard dependency.
