In Compute-First Networking (CFN), an Access Point (AP) makes task offloading decisions based on resource state information reported by a Service Node (SN). A fundamental challenge arises from the trade-off between update overhead and decision accuracy: Frequent state updates consume limited network resources, while infrequent updates lead to stale state views and degraded task performance, especially under high system load. Existing approaches based on periodic updates or Age of Information (AoI) mainly focus on temporal freshness and often overlook whether a state change is actually relevant to offloading decisions. This paper proposes SenseCFN, a decision-aware state synchronization framework for CFN. Instead of synchronizing raw resource states, SenseCFN focuses on identifying state changes that are likely to alter offloading decisions. To this end, we introduce a lightweight semantic state representation that captures decision-relevant system characteristics, along with a Semantic Deviation Index (SDI) to quantify the impact of state shifts on decision outcomes. Based on SDI, the SN triggers updates only when significant decision-impacting changes are detected. Meanwhile, the AP performs offloading decisions using cached semantic states with explicit awareness of potential staleness. The update and offloading policies are jointly optimized using a centralized training with distributed execution (CTDE) approach. Simulation results show that SenseCFN maintains a task success rate of up to 99.6% in saturation-prone scenarios, outperforming baseline methods by more than 25%, while reducing status update frequency by approximately 70% to 96%. These results indicate that decision-aware state synchronization provides an effective and practical alternative to purely time-based update strategies in CFN.

翻译：在计算优先网络（CFN）中，接入点（AP）基于服务节点（SN）上报的资源状态信息做出任务卸载决策。一个根本性挑战源于更新开销与决策准确性之间的权衡：频繁的状态更新会消耗有限的网络资源，而不频繁的更新则会导致状态视图过时及任务性能下降，尤其是在高系统负载下。现有的基于周期性更新或信息年龄（AoI）的方法主要关注时间新鲜度，往往忽略了状态变化是否实际与卸载决策相关。本文提出了SenseCFN，一种用于CFN的决策感知状态同步框架。SenseCFN不同步原始资源状态，而是专注于识别可能改变卸载决策的状态变化。为此，我们引入了一种轻量级的语义状态表示，用于捕获与决策相关的系统特征，并提出了语义偏差指数（SDI）来量化状态偏移对决策结果的影响。基于SDI，SN仅在检测到显著的、影响决策的变化时才触发更新。同时，AP使用缓存的语义状态执行卸载决策，并明确意识到其潜在的过时性。更新策略和卸载策略通过集中式训练与分布式执行（CTDE）方法进行联合优化。仿真结果表明，在易饱和场景中，SenseCFN能维持高达99.6%的任务成功率，比基线方法高出25%以上，同时将状态更新频率降低了约70%至96%。这些结果表明，在CFN中，决策感知的状态同步为纯基于时间的更新策略提供了一种有效且实用的替代方案。