PUBLICATION
Evaluating Trust-Related Principles in an Implemented Distributed Edge AI System
Type
Conference Paper
Year
2025
Authors
Event
20th Swedish National Computer Networking and Cloud Computing Workshop (SNCNW 2025)
Published in
SNCNW 2025
Abstract
The fast expansion of AI within distributed computing environments emphasizes questions about trustworthiness, particularly in contexts involving sensitive data and resourceconstrained edge devices. To address this, we implement and evaluate a lightweight federated learning intrusion detection system in a realistic smart home scenario that combines TensorFlow Lite inference on edge devices, MQTT-based publishing, and coordinated training via the Flower framework. By operationalizing a previously proposed taxonomy and ontology of trustworthy AI in distributed systems, the implementation in this paper demonstrates how key trust dimensions, such as data integrity, model reliability, and process transparency, can be realized in edge environments. Our implementation utilizes local inference with TensorFlow Lite on IoT devices and coordinated federated evaluation via the Flower framework. We also introduce a trust score to quantify how the implementation aligns with the trust principles. The results indicate that the trust mechanisms are maintained without compromising accuracy or loss, contributing to practical insights into the application of theoretical trust frameworks within distributed AI systems.