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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
今后应助灵巧妙柏采纳,获得10
刚刚
coconutluv77发布了新的文献求助10
刚刚
一二三砰发布了新的文献求助10
2秒前
哎呀完成签到,获得积分10
2秒前
3秒前
4秒前
5秒前
脑洞疼应助默顿的笔记本采纳,获得10
5秒前
5秒前
wonder123发布了新的文献求助10
5秒前
温暖雨灵完成签到,获得积分20
6秒前
iNk应助YellowStar采纳,获得10
6秒前
辛辛应助麦子采纳,获得10
7秒前
7秒前
然12138发布了新的文献求助10
8秒前
hanghang完成签到,获得积分10
8秒前
哎呀发布了新的文献求助10
9秒前
灵巧妙柏完成签到,获得积分10
9秒前
FF完成签到 ,获得积分10
9秒前
9秒前
10秒前
好滴捏发布了新的文献求助10
10秒前
14秒前
15秒前
上官若男应助ddddd采纳,获得10
15秒前
16秒前
贤惠的白开水完成签到 ,获得积分10
16秒前
圆圆完成签到 ,获得积分10
16秒前
光亮语梦完成签到 ,获得积分10
16秒前
小白完成签到 ,获得积分10
20秒前
王维佳发布了新的文献求助10
20秒前
Orange应助认真初之采纳,获得10
20秒前
金鱼发布了新的文献求助10
21秒前
21秒前
21秒前
22秒前
22秒前
研究牛王完成签到,获得积分20
23秒前
Rondab应助coconutluv77采纳,获得10
23秒前
阿波罗完成签到,获得积分10
24秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989550
求助须知:如何正确求助?哪些是违规求助? 3531774
关于积分的说明 11254747
捐赠科研通 3270278
什么是DOI,文献DOI怎么找? 1804966
邀请新用户注册赠送积分活动 882125
科研通“疑难数据库(出版商)”最低求助积分说明 809176