Energy-Aware, Device-to-Device Assisted Federated Learning in Edge Computing

计算机科学 边缘设备 边缘计算 上传 推论 启发式 GSM演进的增强数据速率 人工智能 移动边缘计算 深度学习 能源消耗 供应 人工神经网络 移动设备 高效能源利用 机器学习 计算机网络 云计算 操作系统 生物 生态学 电气工程 工程类
作者
Yuchen Li,Weifa Liang,Jing Li,Xiuzhen Cheng,Dongxiao Yu,Albert Y. Zomaya,Song Guo
出处
期刊:IEEE Transactions on Parallel and Distributed Systems [Institute of Electrical and Electronics Engineers]
卷期号:34 (7): 2138-2154 被引量:14
标识
DOI:10.1109/tpds.2023.3277423
摘要

The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and the rise of edge intelligence enables provisioning real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge computing environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this article, we study energy-aware DNN model training in edge computing. We first formulate a novel energy-aware, Device-to-Device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and energy capacity on each IoT device. We then devise a near-optimal learning algorithm for the problem when the training data follows the i.i.d. data distribution. The crux of the proposed algorithm is to explore using the energy of neighboring devices of each device for its local model uploading, by reducing the problem to a series of weighted maximum matching problems in corresponding auxiliary graphs. We also consider the problem without the assumption of the i.i.d. data distribution, for which we propose an efficient heuristic algorithm. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results show that the proposed algorithms are promising.
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