FLight: A lightweight federated learning framework in edge and fog computing

计算机科学 云计算 服务器 边缘计算 分布式计算 雾计算 边缘设备 GSM演进的增强数据速率 架空(工程) 延迟(音频) 计算机网络 操作系统 人工智能 电信
作者
Wuji Zhu,Mohammad Goudarzi,Rajkumar Buyya
出处
期刊:Software - Practice and Experience [Wiley]
卷期号:54 (5): 813-841
标识
DOI:10.1002/spe.3300
摘要

Abstract The number of Internet of Things (IoT) applications, especially latency‐sensitive ones, have been significantly increased. So, cloud computing, as one of the main enablers of the IoT that offers centralized services, cannot solely satisfy the requirements of IoT applications. Edge/fog computing, as a distributed computing paradigm, processes, and stores IoT data at the edge of the network, offering low latency, reduced network traffic, and higher bandwidth. The edge/fog resources are often less powerful compared to cloud, and IoT data is dispersed among many geo‐distributed servers. Hence, Federated Learning (FL), which is a machine learning approach that enables multiple distributed servers to collaborate on building models without exchanging the raw data, is well‐suited to edge/fog computing environments, where data privacy is of paramount importance. Besides, to manage different FL tasks on edge/fog computing environments, a lightweight resource management framework is required to manage different incoming FL tasks while does not incur significant overhead on the system. Accordingly, in this article, we propose a lightweight FL framework, called FLight, to be deployed on a diverse range of devices, ranging from resource‐limited edge/fog devices to powerful cloud servers. FLight is implemented based on the FogBus2 framework, which is a containerized distributed resource management framework. Moreover, FLight integrates both synchronous and asynchronous models of FL. Besides, we propose a lightweight heuristic‐based worker selection algorithm to select a suitable set of available workers to participate in the training step to obtain higher training time efficiency. The obtained results demonstrate the efficiency of the FLight. The worker selection technique reduces the training time of reaching 80% accuracy by 34% compared to sequential training, while asynchronous one helps to improve synchronous FL training time by 64%.
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
橘子发布了新的文献求助10
刚刚
刚刚
乐观寻绿完成签到,获得积分10
刚刚
淡淡智宸发布了新的文献求助10
1秒前
郭慧梅完成签到,获得积分10
1秒前
dmr发布了新的文献求助10
2秒前
sunhealth发布了新的文献求助20
2秒前
4秒前
Max完成签到,获得积分10
4秒前
大个应助NicotineZen采纳,获得10
4秒前
Dream完成签到,获得积分10
4秒前
隐形曼青应助清秀豪英采纳,获得30
5秒前
寒夜寻发布了新的文献求助10
5秒前
6秒前
星星完成签到 ,获得积分10
6秒前
华仔应助加油科研采纳,获得10
6秒前
打打应助水果采纳,获得10
7秒前
淡淡智宸完成签到,获得积分10
8秒前
X75发布了新的文献求助10
9秒前
大尾猫完成签到,获得积分10
9秒前
9秒前
9秒前
JialiZhao完成签到,获得积分10
10秒前
10秒前
11秒前
英吉利25发布了新的文献求助20
11秒前
11秒前
烟花应助自己采纳,获得10
12秒前
12秒前
5477发布了新的文献求助10
12秒前
linmu完成签到 ,获得积分10
13秒前
sigla发布了新的文献求助10
15秒前
类器官牛逼完成签到,获得积分20
15秒前
Yve完成签到,获得积分10
15秒前
科研通AI2S应助嘟嘟采纳,获得10
15秒前
TIGun发布了新的文献求助10
16秒前
东方雨季发布了新的文献求助10
17秒前
sunhealth完成签到,获得积分10
17秒前
清秀豪英发布了新的文献求助30
18秒前
李健的粉丝团团长应助Cici采纳,获得10
18秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
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 3970062
求助须知:如何正确求助?哪些是违规求助? 3514782
关于积分的说明 11175968
捐赠科研通 3250119
什么是DOI,文献DOI怎么找? 1795198
邀请新用户注册赠送积分活动 875630
科研通“疑难数据库(出版商)”最低求助积分说明 804951