Energy-Efficient Connectivity-Aware Learning Over Time-Varying D2D Networks

IF 8.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal of Selected Topics in Signal Processing Pub Date : 2024-03-11 DOI:10.1109/JSTSP.2024.3374591
Rohit Parasnis;Seyyedali Hosseinalipour;Yun-Wei Chu;Mung Chiang;Christopher G. Brinton
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Abstract

Semi-decentralized federated learning blends the conventional device-to-server (D2S) interaction structure of federated model training with localized device-to-device (D2D) communications. We study this architecture over edge networks with multiple D2D clusters modeled as time-varying and directed communication graphs. Our investigation results in two algorithms: (a) a connectivity-aware learning algorithm that controls the fundamental trade-off between the convergence rate of the model training process and the number of energy-intensive D2S transmissions required for global aggregation, and (b) a motion-planning algorithm to enhance the densities and regularity levels of cluster digraphs so as to further reduce the number of D2S transmissions in connectivity-aware learning. Specifically, in our semi-decentralized methodology, weighted-averaging-based D2D updates are injected into the federated averaging framework based on column-stochastic weight matrices that encapsulate the connectivity within the clusters. To develop our algorithm, we show how the current expected optimality gap (i.e., the distance between the most recent global model computed by the server and the target/desired optimal model) depends on the greatest two singular values of the weighted adjacency matrices (and hence on the densities and degrees of digraph regularity) of the D2D clusters. We then derive tight bounds on these singular values in terms of the node degrees of the D2D clusters, and we use the resulting expressions to design our connectivity-aware learning algorithm. Simulations performed using real-world datasets and Random Direction Mobility Model (RDMM)-based time-varying D2D topologies reveal that our connectivity-aware algorithm significantly reduces the total communication energy required to reach a target accuracy level compared with baselines while achieving the accuracy level in nearly the same number of iterations as these baselines.
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时变 D2D 网络上的高能效连接感知学习
半分散联合学习将联合模型训练的传统设备到服务器(D2S)交互结构与本地化设备到设备(D2D)通信相结合。我们在边缘网络上研究了这一架构,该网络具有多个 D2D 集群,这些集群被建模为时变的有向通信图。我们的研究产生了两种算法:(a) 一种连接性感知学习算法,可控制模型训练过程的收敛速度与全局聚合所需的高能耗 D2S 传输数量之间的基本权衡;(b) 一种运动规划算法,可提高集群数字图的密度和规则性水平,从而进一步减少连接性感知学习中的 D2S 传输数量。具体来说,在我们的半去中心化方法中,基于加权平均的 D2D 更新被注入到基于列随机权重矩阵的联合平均框架中,该权重矩阵封装了集群内的连接性。为了开发我们的算法,我们展示了当前的预期最优差距(即服务器计算的最新全局模型与目标/期望最优模型之间的距离)如何取决于 D2D 群集的加权邻接矩阵的最大两个奇异值(因此也取决于密度和数图规则度)。然后,我们根据 D2D 簇的节点度推导出了这些奇异值的紧约束,并使用由此得到的表达式设计了我们的连接感知学习算法。使用真实世界数据集和基于随机方向移动模型(RDMM)的时变 D2D 拓扑进行的仿真表明,与基线算法相比,我们的连接性感知算法大大降低了达到目标准确度水平所需的总通信能量,同时在与这些基线算法几乎相同的迭代次数内达到了准确度水平。
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来源期刊
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing 工程技术-工程:电子与电气
CiteScore
19.00
自引率
1.30%
发文量
135
审稿时长
3 months
期刊介绍: The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others. The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.
期刊最新文献
Front Cover Table of Contents IEEE Signal Processing Society Information Introduction to the Special Issue Near-Field Signal Processing: Algorithms, Implementations and Applications IEEE Signal Processing Society Information
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