[Submitted on 13 Oct 2026]
Abstract:Resource-constrained IoT devices cache learned models for real-time decisions, but frequent updates incur substantial bandwidth, energy, and service overhead. Because decision-relevant model differences need not grow with elapsed time, we define Age of Intelligence (AoInt) as a model-version-relative, client-specific, and action utility distortion, and formulate synchronization as a tradeoff between stale-decision regret and refresh cost. Furthermore, we develop Kullback-Leibler-aware AoInt Synchronization (KL-AoIntSync), which accumulates output-distribution changes using client-specific unlabeled probes, whereas Parameter-aware AoInt Synchronization (Param-AoIntSync) uses a server-broadcast scalar parameter change. Then, we establish pathwise guarantees with fixed client distributions, action sets, and utilities. The numerical results show that calibrated Param-AoIntSync reduces joint synchronization cost by 6.15% relative to an Instant-Parameters policy while performing comparably to KL-AoIntSync.
Submission history
From: Ming Shi Prof. [view email]
[v1] Tue, 13 Oct 2026 15:56:11 UTC (220 KB)
