ArticleScientific reports2026
Confidence-calibrated federated graph attention for internet of things agents under latency SLOs.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Internet of Things (IoT) agents that trigger network enforcement actions must be both well-calibrated (for safe triage) and tail-latency predictable (for service level objectives, SLOs). We present Confidence-Calibrated HP-FedGAT-Trust-IBN, a federated, graph-attention architecture that closes the loop from IoMT sensing to SDN enforcement via parameter-efficient (LoRA/PEFT) updates ([Formula: see text] MB/round), trust-weighted secure aggregation, and intent verification (IBN) triage. Evaluation follows a two-plane protocol: a learning plane with [Formula: see text] simulated clients under a matched comparator harness (Graph-FL and uncertainty-aware FL baselines), and a serving plane that replays exported checkpoints on real edge devices (Raspberry Pi 5, Jetson Orin Nano, Intel NUC 11) and validates SLOs using hardware ECDFs and empirical [Formula: see text]. The model achieves high discrimination (ROC-AUC/PR-AUC [Formula: see text]-[Formula: see text]) with improved calibration (low ECE) under the matched harness, while the serving loop satisfies the [Formula: see text] ms requirement by device-measured [Formula: see text] (e.g., enforcement [Formula: see text] ms, vs. [Formula: see text] ms for an efficient-UQ baseline) and explicit compliance [Formula: see text]. The latency decomposition includes all calibration costs and Monte-Carlo expectations ([Formula: see text], with measured MC share reported), and security modes are quantified end-to-end: CKKS + SMPC adds device-measured [Formula: see text] and crypto-attributable Joules (e.g., [Formula: see text] ms and [Formula: see text] J/round on Raspberry Pi 5). Energy/round is measured on identical hardware and mapped to CO
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