arXiv 2310.074972025-07-22cs.LGcs.AI

通过样本驱动控制实现联邦持续学习的节能实时感知

Energy-Efficient and Real-Time Sensing for Federated Continual Learning via Sample-Driven Control

School of Electrical and Electronic Engineering, Hanoi University of Science and Technology（电子与电气工程学院，河内科学技术大学）；Department of Intelligent Computing and Data Science, VinUniversity（智能计算与数据科学系，文大学）；Department of Electrical and Computer Engineering, University of Saskatchewan（电气与计算机工程系，萨斯喀彻温大学）；School of Electrical and Data Engineering, University of Technology Sydney（电气与数据工程学院，悉尼技术大学）；School of Electricals and Electronics, Hanoi University of Science and Technology（电子与电气工程学院，河内科学技术大学）；School of Computer Science and Statistics, Trinity College Dublin（计算机科学与统计学学院，都柏林信任学院）

Minh Ngoc Luu, Minh-Duong Nguyen, Ebrahim Bedeer, Van Duc Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Quoc-Viet Pham

备注：: 20 pages, 7 figures

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AI总结：

针对联邦持续学习中实时感知导致的能耗与资源问题，提出SCFL样本驱动控制框架及A2C-EI算法，在保证收敛和及时传输的同时节能至多85%。

摘要：

智能实时感知（RTS）系统必须持续获取、更新、整合并应用知识，以适应真实世界的动态变化。在此背景下管理分布式智能需要联邦持续学习（FCL）。然而，在FCL系统中有效捕捉RTS数据的多样特征面临重大挑战，包括严重影响计算和通信资源、推高能源成本，并最终降低整体系统性能。为克服这些挑战，我们利用“泛化差距”概念，研究从理想RTS场景到实际RTS场景的数据分布偏移如何影响人工智能（AI）模型性能。借此，我们可以分析RTS中的采样时间如何与AI性能下降、计算成本和通信效率相关联。基于这一观察，我们开发了一种新颖的面向联邦持续学习的样本驱动控制（SCFL）技术，专为具备RTS能力的移动边缘网络设计。具体而言，SCFL是一个优化问题，利用采样过程在维持FCL框架能效的同时，最小化泛化差距并提高整体准确率。为求解这一高度复杂且时变的优化问题，我们提出了一种具有显式和隐式约束的新型soft actor-critic算法（A2C-EI）。实验表明，与其他深度强化学习（DRL）基线相比，我们的方法可实现更高效率。值得注意的是，SCFL能够将能耗显著降低至多85%，同时保持FL收敛和及时的数据传输。

英文摘要：

An intelligent Real-Time Sensing (RTS) system must continuously acquire, update, integrate, and apply knowledge to adapt to real-world dynamics. Managing distributed intelligence in this context requires Federated Continual Learning (FCL). However, effectively capturing the diverse characteristics of RTS data in FCL systems poses significant challenges, including severely impacting computational and communication resources, escalating energy costs, and ultimately degrading overall system performance. To overcome these challenges, we investigate how the data distribution shift from ideal to practical RTS scenarios affects Artificial Intelligence (AI) model performance by leveraging the \textit{generalization gap} concept. In this way, we can analyze how sampling time in RTS correlates with the decline in AI performance, computation cost, and communication efficiency. Based on this observation, we develop a novel Sample-driven Control for Federated Continual Learning (SCFL) technique, specifically designed for mobile edge networks with RTS capabilities. In particular, SCFL is an optimization problem that harnesses the sampling process to concurrently minimize the generalization gap and improve overall accuracy while upholding the energy efficiency of the FCL framework. To solve the highly complex and time-varying optimization problem, we introduce a new soft actor-critic algorithm with explicit and implicit constraints (A2C-EI). Our empirical experiments reveal that we can achieve higher efficiency compared to other DRL baselines. Notably, SCFL can significantly reduce energy consumption up to $85\%$ while maintaining FL convergence and timely data transmission.

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。