Industrial applications, such as real-time manufacturing, fault classification and inference, autonomous cars, etc., are data-driven applications that require machine learning with a wealth of data generated from industrial Internet of Things (IoT) devices. However, conventional approaches of transmitting this rich data to a remote data center to learn may be undesired due to the non-negligible network transmission delay and the sensitiveness of data privacy. By deploying a number of computing-capable devices at the network edge, edge computing supports the implementation of machine learning close to the industrial environment. Considering the heterogeneous computing capability as well as network location of edge devices, there are two types of feasible edge computing based machine learning models, including the centralized learning and federated learning models. In centralized learning, a resource-rich edge server aggregates the data from different IoT devices and performs machine learning. In federated learning, distributed edge devices and a federated server collaborate to perform machine learning. The features that data should be offloaded in centralized learning while it is locally trained in federated learning make centralized learning and federated learning quite different. We study the computation offloading problem for edge computing based machine learning in an industrial environment, considering the abovementioned machine learning models. We formulate a machine learning-based offloading problem with the goal of minimizing the training delay. Then, an energy-constrained delay-greedy (ECDG) algorithm is designed to solve the problem. Finally, simulation studies based on the MNIST dataset have been conducted to illustrate the efficiency of the proposal.
[1]
Takayuki Nishio,et al.
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
,
2018,
ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
[2]
Blaise Agüera y Arcas,et al.
Federated Learning of Deep Networks using Model Averaging
,
2016,
ArXiv.
[3]
Sherali Zeadally,et al.
Deploying Fog Computing in Industrial Internet of Things and Industry 4.0
,
2018,
IEEE Transactions on Industrial Informatics.
[4]
N. Jazdi,et al.
Cyber physical systems in the context of Industry 4.0
,
2014,
2014 IEEE International Conference on Automation, Quality and Testing, Robotics.
[5]
Lei Li,et al.
Energy-Efficient and Delay-Guaranteed Workload Allocation in IoT-Edge-Cloud Computing Systems
,
2019,
IEEE Access.
[6]
Tim Verbelen,et al.
A Survey on Distributed Machine Learning
,
2019,
ACM Comput. Surv..
[7]
Kin K. Leung,et al.
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
,
2018,
IEEE Journal on Selected Areas in Communications.
[8]
Kaibin Huang,et al.
Towards an Intelligent Edge: Wireless Communication Meets Machine Learning
,
2018,
ArXiv.
[9]
Tianjian Chen,et al.
Federated Machine Learning: Concept and Applications
,
2019
.
[10]
Jason P. Jue,et al.
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms
,
2019
.