通过事件触发计算卸载实现通信高效的协同边缘 AI
Communication Efficient Cooperative Edge AI via Event-Triggered Computation Offloading
AI总结：
针对6G任务关键型应用中罕见事件边缘推理的通信瓶颈，提出信道自适应事件触发框架，通过双阈值多出口架构、在线阈值优化和强凸重构实现高准确率与低开销。
摘要：
罕见事件尽管发生频率低，却往往携带关键信息，并在自动驾驶、医疗保健和工业自动化等任务关键型应用中需要立即关注。这些任务的数据密集型特性及其对快速响应的需求，再加上边缘 AI（或称边缘推理）的设计，给系统和技术带来了重大挑战。现有边缘推理方法往往因高维数据传输而遭遇通信瓶颈，并且无法对罕见事件提供及时响应，从而限制了其在第六代（6G）移动网络任务关键型应用中的有效性。为克服这些挑战，我们提出了一种信道自适应、事件触发的边缘推理框架，该框架优先处理高效的罕见事件。该框架的核心是双阈值多出口架构，它能够对本地检测到的罕见事件进行早期本地推理，同时将更复杂的罕见事件卸载到边缘服务器进行详细分类。为进一步提升系统性能，我们开发了一种信道自适应卸载策略，并配合在线算法动态确定用于控制卸载决策的最佳置信度阈值。通过将原始非凸函数重构为等价的强凸函数，求解了相关优化问题。利用深度神经网络分类器和真实医疗数据集，实验表明，与现有边缘推理方法相比，所提出的框架不仅实现了更优的罕见事件分类准确率，还有效降低了通信开销。
英文摘要：
Rare events, despite their infrequency, often carry critical information and require immediate attentions in mission-critical applications such as autonomous driving, healthcare, and industrial automation. The data-intensive nature of these tasks and their need for prompt responses, combined with designing edge AI (or edge inference), pose significant challenges in systems and techniques. Existing edge inference approaches often suffer from communication bottlenecks due to high-dimensional data transmission and fail to provide timely responses to rare events, limiting their effectiveness for mission-critical applications in the sixth-generation (6G) mobile networks. To overcome these challenges, we propose a channel-adaptive, event-triggered edge-inference framework that prioritizes efficient rare-event processing. Central to this framework is a dual-threshold, multi-exit architecture, which enables early local inference for rare events detected locally while offloading more complex rare events to edge servers for detailed classification. To further enhance the system's performance, we developed a channel-adaptive offloading policy paired with an online algorithm to dynamically determine the optimal confidence thresholds for controlling offloading decisions. The associated optimization problem is solved by reformulating the original non-convex function into an equivalent strongly convex one. Using deep neural network classifiers and real medical datasets, our experiments demonstrate that the proposed framework not only achieves superior rare-event classification accuracy, but also effectively reduces communication overhead, as opposed to existing edge-inference approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
