# QoC-Driven MEC Transfer System Framework in Wireless Networks

Authors: Ping Yu; Hongwei Zhao; Ming Hu; Hui Yan; Xiaozhong Geng; Hanlin Chen; Dejin Chu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In this paper, we propose a HeterogeneousMEC System Framework based on Transfer Learning (HMECSF-TL), which uses convolutional neural network (CNN) to process few training samples. In view of the time-varying network environment and the limited end devices resources, the HMECSF-TL framework uses transfer learning (TL) technology to optimize the CNN model and jointly optimizes the allocation of computing resources and communication resources, which is beneficial to achieve the dual goals of extending the use time of end devices and improving the speed and the accuracy of image classification. We first introduce the Quality of Content (QoC)-driven MEC transfer system architecture of cloud-edge-end. The cloud server uses the existing image dataset to train the general neural network model in advance and transfer the general model to the edge servers, and then the edge servers deploy the local models to the end devices to form the personalized models. Then, considering the time-varying situation of the network environment, in order to get the updated model faster and better, we present the process of collaborative optimization of model between the edge sever and multiple end devices, using an edge server as an example. Considering the limited resources of the end devices, we propose a joint optimization of energy and latency with the goal of minimizing offloading cost, in order to rapidly improve the speed and the accuracy of image classification with few training samples under the premise of rational resource allocation and verify the performance of the framework experimentally. Simulation results show that the proposed HMECSF-TL framework outperforms the benchmark strategy without TL in terms of reducing the model training time and improving the image classification accuracy, as well as reducing the offloading cost.


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## Body

## 1. Introduction

The rapid development ofIoT technology and the emergence of massive data have led to a shift in the data processing model from a centralized cloud data center to distributed network edge sides, resulting in the emergence of multiaccess edge computing (MEC). Distributed MEC servers provide computing resources and IT services with low-latency, low energy consumption, and high bandwidth and security. MEC technology has become an important technology for 5G. At the same time, with the popularization of IoT devices and the intelligentization of mobile terminals, more and more mobile applications have become resource-scarce and latency-sensitive. Only with the continuous enhancement of the data processing capabilities of the end intelligent devices can they better satisfy the needs. MEC technology makes better use of data through distributed model training and intelligent knowledge inference, enabling deeper integration of IoT and AI and further realizing the intelligent connection of all things.With the help ofMEC, end devices can offload computing tasks from computing-intensive applications such as image classification to nearby edge servers to save energy and improve computing power [1]. CNN is a key technology to achieve image classification, and it is an influential deep learning framework. The training process of CNN models based on big data introduces a relatively high computational complexity, and MEC is beneficial in reducing the computational burden of the devices. In addition, in practical applications, the training data and the data in the target domain may not be in the same feature space or may follow different data distributions. TL is suitable for solving the above problems, and TL can well reduce the convergence time of models in the target domain [2]. Although TL has been shown to improve data-starved situations, the huge demand for personalized samples and unstable transmission latency limit the performance of real-time applications. Although MEC-assisted deep learning can provide responsive services and sufficient computing resources, related research is still in its infancy [3]. Especially for computing-intensive real-time applications requiring ultralow latency, it is important to explore the application of TL in MEC systems. This paper is based on this background to carry out research work.In order to alleviate the contradiction between the limited resources of the end devices and efficient image classification, we explore the relationship between energy consumption, latency, andQoC; then we consider offline training of the general model, online collaborative optimization of local models, and fine-tuning of personalized models; and also consider the requirements of energy consumption and latency; and finally, we propose a cloud-side-end MEC transfer system framework. We further study the training and inference work of the deep learning model by implementing model transfer through TL for energy-constrained end devices under the condition of limited wireless bandwidth. The advantages of the framework and the main contributions of this paper are summarized as follows:
(1)
Taking full advantage of the abundant resources on thecloud-edge and the latest data provided by end devices, we propose HMECSF-TL based on the cloud-edge-end architecture. The proposed framework completes the model transfer through TL and localizes and personalizes the general model of deep neural network in order to achieve the effect of reducing model training time(2)
The framework adopts an online collaborative way to train the local model on the edge server, which can not only respond quickly on the edge server, but also improve the accuracy of image classification. Specifically, the end devices that satisfy the optimal resource allocation strategy on the edge server offload few training samples to participate in the collaborative training of the local model on the edge server. After training to obtain the optimized model, the personalized model of the end devices are fine-tuned throughTL to update it to the optimized model, and then the inference work of the end devices that do not participate in the collaborative work is completed(3)
TheHMECSF-TL framework considers the balance between improving image classification accuracy and rational allocation of resources. Specifically, considering the limited energy of end devices and limited wireless bandwidth in wireless networks, we propose a joint optimization energy and latency problem to implement resource management and find optimal strategy for bandwidth allocation and task offloading to minimize the weighted sum of costs(4)
Simulation experiments show that the proposedHMECSF-TL framework can achieve better performance, which is embodied in the advantages of cost-saving and the model performance related to the image classification accuracy and the training timeThe rest of the paper is organized as follows.Part 2 describes the related work. Part 3 describes the proposed framework and the system model in detail. Part 4 presents the problem formulation of optimizing resources. Part 5 presents the problem of image classification. Part 6 gives the simulation results and the conclusions drawn. Part 7 concludes.


## 2. Related Work

MEC is an evolution of cloud computing that sinks computing power to the edge of the network. In [4], MEC enables end devices to offload intensive computing task to nearby edge server to save energy consumption and improve computing power. The edge-end computing framework can provide better low-latency and stronger computing power, which is suitable for solving various applications with low-latency and computing-intensive. For example, image classification using CNN is one of the common applications, which has a large impact on the performance improvement in terms of classification accuracy. In [5], an image classification using CNN is provided by mobile devices for various image and video analysis applications. However, due to the limited computing resources of end devices, the devices are insufficient to provide accurate and fast image classification capabilities. In [6], an image classification method based on the MEC architecture was proposed to solve the problem of limited computing resources of devices. 5G provides ultra-low-latency, high-reliability connections for the end devices. In [7], Kim et al. leveraged the low transmission latency of 5G by combining 5G and MEC to bring the computing power closer to the end devices and reduce the applications latency. Thus, the combination of 5G and MEC makes it excellent for various image and video analysis applications.It is worth noting that most machine learning algorithms usually assume that the training data and test data lie in the same feature space and have the same distribution [8]. However, in [9], it is proposed that the input space may differ between the source tasks and the target tasks, the situation that is more realistic. And TL can be a good solution to the problems such as different input spaces. The deep transfer learning method, which uses deep learning technology to build transfer learning model, has become an important method to solve the problems of deep learning data dependency and insufficient training data. The deep learning model can greatly reduce the interference of human experience and improve accuracy under certain conditions [10]. In [11], a deep transfer network model based on a joint distribution adaptation method was proposed. In [12], a sparse encoder was combined with a deep transfer learning method to predict device lifetime. In [13], the use of deep GANs was proposed to solve the domain adaption problem. In this paper, TL is used in a cloud-side-end computing framework to reduce the model training latency and further improve the performance of the framework.


## 3. Heterogeneous MEC System Framework Based on Transfer Learning (HMECSF-TL)

### 3.1. System Architecture

Although the image processing work such as image classification based on deep learning has high recognition accuracy, it usually requires powerful computing resources to achieve the desired results, such as using cloud computing to process massive data. It is worth noting that cloud computing brings the problem of excessive communication latency and the risk of leaking user privacy. In recent years, with the rapid development ofMEC, AI, 5G, and other technologies, edge intelligence has emerged as the times require, communication latency and privacy risks can be well resolved, and network functions have shifted from interconnected devices to interconnected intelligence, but there are still some problems to be solved. Using the cloud-edge-end architecture to deploy the general model to edge servers and end devices can achieve edge intelligence, and latency requirement and privacy protection are guaranteed to a certain extent. However, due to the limited data collected by various end devices, the general model established by the deep learning algorithm on the cloud server usually has low accuracy in processing few samples, resulting in poor generalization of the model. Therefore, it is necessary to optimize the model by combining real-time data in order to achieve the expected effect. We perform model transfer on cloud-side-end through TL to improve the model training speed. The accuracy of model can be quickly improved by adopting two stages: the edge-end online collaborative optimization model method and the cloud-edge offline optimization model method. Considering the abundant resources of cloud server and edge servers but limited resources of end devices, this paper focuses on the edge-end online collaborative optimization model stage. The system model of our proposed HMECSF-TL framework is shown in Figure 1.Figure 1
System model.


#### 3.1.1. The Description of theHMECSF-TL Framework

This system includes the cloud server, the edge servers, and the end devices. ThroughTL, we transfer the general model on the cloud to the edge servers as the local models and then transfer the local model to the end devices as the personalized models [3]. It is worth noting that each time the upper model is optimized, the lower models are then updated by fine-tuning actions. We use massive data to train the general model on the cloud and use the data offloaded by multiple end devices to collaboratively train the local model on the edge server. The end devices that have not participated in the collaborative training use the personalized model to complete the image inference on the local few samples after fine-tuning. The general flow of the system optimization model is further described next.
(a)
Each end device collects few samples, and then some of the end devices that satisfy the allocation strategy of jointly optimizing energy consumption and latency offload few samples to the edge server, and then theedge-end online cooperation completes the training of the local model(b)
The edge server uses the trained local model to update the personalized models of end devices through fine-tuning operations, and the devices that have not participated in the collaborative training of the local model use the updated personalized model to continue to complete the inference work(c)
Each edge server asynchronously offloads the dataset formed by the data offload by the end devices to the cloud server at regular intervals in order to optimize the general model. The optimized general model on the cloud also updates the local model of each edge server through fine-tuning operation, in order to improve the performance of the local model faster


#### 3.1.2. Online Collaborative Training Stage ofEdge-End

In this paper, we focus on theedge-end collaborative training stage which is closely related to the end devices, and the model optimization process is shown in Figure 2.Figure 2
Model online optimization.The optimization process includes the following four steps [14].
(a)
Offload resource parameters to get the offload strategy and bandwidth allocation. The edge server calculates the optimal offload strategy and bandwidth allocation according to some key parameters such as the required computing resources of the processing task, preprocessed data size, transmission power, and channel conditions and then notifies the end devices(b)
Few samples offload from the end devices. The end devices that satisfy the offload strategy use the allocated bandwidth to offload tasks to the edge server(c)
Optimize the local model. The local model is trained on the edge server using few samples offloaded from multiple end devices at a small learning rate until convergence to further optimize the local model(d)
Broadcast the optimized local model. The edge server broadcasts the well-trained local model to the end devices, so that each device can independently perform inference work such as image classification using the updated personalized model and those end devices that do not participate in the collaborative training continue to complete the inference workAt this stage, because each device may have unique training samples, the edge server usually tends to include more samples from the devices. However, considering that the end devices have limited energy and limited wireless bandwidth, only a part of the devices can actually offload their few samples to the edge server. Therefore, we consider which devices to offload and how to allocate the appropriate wireless bandwidth.


## 3.1. System Architecture

Although the image processing work such as image classification based on deep learning has high recognition accuracy, it usually requires powerful computing resources to achieve the desired results, such as using cloud computing to process massive data. It is worth noting that cloud computing brings the problem of excessive communication latency and the risk of leaking user privacy. In recent years, with the rapid development ofMEC, AI, 5G, and other technologies, edge intelligence has emerged as the times require, communication latency and privacy risks can be well resolved, and network functions have shifted from interconnected devices to interconnected intelligence, but there are still some problems to be solved. Using the cloud-edge-end architecture to deploy the general model to edge servers and end devices can achieve edge intelligence, and latency requirement and privacy protection are guaranteed to a certain extent. However, due to the limited data collected by various end devices, the general model established by the deep learning algorithm on the cloud server usually has low accuracy in processing few samples, resulting in poor generalization of the model. Therefore, it is necessary to optimize the model by combining real-time data in order to achieve the expected effect. We perform model transfer on cloud-side-end through TL to improve the model training speed. The accuracy of model can be quickly improved by adopting two stages: the edge-end online collaborative optimization model method and the cloud-edge offline optimization model method. Considering the abundant resources of cloud server and edge servers but limited resources of end devices, this paper focuses on the edge-end online collaborative optimization model stage. The system model of our proposed HMECSF-TL framework is shown in Figure 1.Figure 1
System model.


### 3.1.1. The Description of theHMECSF-TL Framework

This system includes the cloud server, the edge servers, and the end devices. ThroughTL, we transfer the general model on the cloud to the edge servers as the local models and then transfer the local model to the end devices as the personalized models [3]. It is worth noting that each time the upper model is optimized, the lower models are then updated by fine-tuning actions. We use massive data to train the general model on the cloud and use the data offloaded by multiple end devices to collaboratively train the local model on the edge server. The end devices that have not participated in the collaborative training use the personalized model to complete the image inference on the local few samples after fine-tuning. The general flow of the system optimization model is further described next.
(a)
Each end device collects few samples, and then some of the end devices that satisfy the allocation strategy of jointly optimizing energy consumption and latency offload few samples to the edge server, and then theedge-end online cooperation completes the training of the local model(b)
The edge server uses the trained local model to update the personalized models of end devices through fine-tuning operations, and the devices that have not participated in the collaborative training of the local model use the updated personalized model to continue to complete the inference work(c)
Each edge server asynchronously offloads the dataset formed by the data offload by the end devices to the cloud server at regular intervals in order to optimize the general model. The optimized general model on the cloud also updates the local model of each edge server through fine-tuning operation, in order to improve the performance of the local model faster


### 3.1.2. Online Collaborative Training Stage ofEdge-End

In this paper, we focus on theedge-end collaborative training stage which is closely related to the end devices, and the model optimization process is shown in Figure 2.Figure 2
Model online optimization.The optimization process includes the following four steps [14].
(a)
Offload resource parameters to get the offload strategy and bandwidth allocation. The edge server calculates the optimal offload strategy and bandwidth allocation according to some key parameters such as the required computing resources of the processing task, preprocessed data size, transmission power, and channel conditions and then notifies the end devices(b)
Few samples offload from the end devices. The end devices that satisfy the offload strategy use the allocated bandwidth to offload tasks to the edge server(c)
Optimize the local model. The local model is trained on the edge server using few samples offloaded from multiple end devices at a small learning rate until convergence to further optimize the local model(d)
Broadcast the optimized local model. The edge server broadcasts the well-trained local model to the end devices, so that each device can independently perform inference work such as image classification using the updated personalized model and those end devices that do not participate in the collaborative training continue to complete the inference workAt this stage, because each device may have unique training samples, the edge server usually tends to include more samples from the devices. However, considering that the end devices have limited energy and limited wireless bandwidth, only a part of the devices can actually offload their few samples to the edge server. Therefore, we consider which devices to offload and how to allocate the appropriate wireless bandwidth.


## 3.1.1. The Description of theHMECSF-TL Framework

This system includes the cloud server, the edge servers, and the end devices. ThroughTL, we transfer the general model on the cloud to the edge servers as the local models and then transfer the local model to the end devices as the personalized models [3]. It is worth noting that each time the upper model is optimized, the lower models are then updated by fine-tuning actions. We use massive data to train the general model on the cloud and use the data offloaded by multiple end devices to collaboratively train the local model on the edge server. The end devices that have not participated in the collaborative training use the personalized model to complete the image inference on the local few samples after fine-tuning. The general flow of the system optimization model is further described next.
(a)
Each end device collects few samples, and then some of the end devices that satisfy the allocation strategy of jointly optimizing energy consumption and latency offload few samples to the edge server, and then theedge-end online cooperation completes the training of the local model(b)
The edge server uses the trained local model to update the personalized models of end devices through fine-tuning operations, and the devices that have not participated in the collaborative training of the local model use the updated personalized model to continue to complete the inference work(c)
Each edge server asynchronously offloads the dataset formed by the data offload by the end devices to the cloud server at regular intervals in order to optimize the general model. The optimized general model on the cloud also updates the local model of each edge server through fine-tuning operation, in order to improve the performance of the local model faster


## 3.1.2. Online Collaborative Training Stage ofEdge-End

In this paper, we focus on theedge-end collaborative training stage which is closely related to the end devices, and the model optimization process is shown in Figure 2.Figure 2
Model online optimization.The optimization process includes the following four steps [14].
(a)
Offload resource parameters to get the offload strategy and bandwidth allocation. The edge server calculates the optimal offload strategy and bandwidth allocation according to some key parameters such as the required computing resources of the processing task, preprocessed data size, transmission power, and channel conditions and then notifies the end devices(b)
Few samples offload from the end devices. The end devices that satisfy the offload strategy use the allocated bandwidth to offload tasks to the edge server(c)
Optimize the local model. The local model is trained on the edge server using few samples offloaded from multiple end devices at a small learning rate until convergence to further optimize the local model(d)
Broadcast the optimized local model. The edge server broadcasts the well-trained local model to the end devices, so that each device can independently perform inference work such as image classification using the updated personalized model and those end devices that do not participate in the collaborative training continue to complete the inference workAt this stage, because each device may have unique training samples, the edge server usually tends to include more samples from the devices. However, considering that the end devices have limited energy and limited wireless bandwidth, only a part of the devices can actually offload their few samples to the edge server. Therefore, we consider which devices to offload and how to allocate the appropriate wireless bandwidth.


## 4. Problem Formulation of Optimizing Resources

TheHMECSF-TL framework improves the model training speed through TL and uses collaborative optimization to improve the model accuracy. However, considering the limited energy of the end devices and the limited wireless bandwidth, only some of the end devices can actually offload tasks to participate in the collaborative training local model. So we focus on the edge-end collaborative training model phase and obtain the optimal strategy of bandwidth allocation and task unloading by minimizing the weighted sum of costs and then perform edge-end collaborative training to complete the image classification work. Minimizing the weighted sum of costs is a problem of jointly optimizing energy consumption and latency, which is described further below.We take an edge server as an example to focus on theedge-end collaborative training model stage. There are M terminal devices within the coverage of the edge server, and the edge server has sufficient computing resources and storage resources, and each end device contains embedded sensor and embedded computing chip, and each end device is involved in only one task in the same time slot.The task for them-th device is represented as
(1)Um=Fm,Dm,Treq,m∈M,where Fm indicates the computing resources required by the task, Dm indicates the size of the data to be processed, and Treq is the latency constraint.Whether them-th device offloads the task to the edge server can be indicated by the variable am,
(2)am=0,1,m∈M.If the task is processed on the device side, thenam=0. We use fmL to represent the computing execution capacity of the m-th device for processing the task Um. Fm,maxL represents the maximum computing resource of the m-th device, which should satisfy the following constraint.
(3)fmL≤Fm,maxL,m∈M.If them-th device offloads the task Um to the edge server, then am=1 . Here we assume that the device can only select at most one server for offloading and that it performs the full offload operation.We usefmE to represent the computing execution capacity assigned to the m-th device by the edge server, which is used to process task Um. Fmax represents the maximum computing resources of the edge server, which should satisfy the following constraint.
(4)∑m=1MamfmE≤Fmax,m∈M.


### 4.1. Latency Model

If them-th device Dm processes the task Um on the device side, then the latency only involves the calculated latency TmL which is represented as
(5)TmL=FmfmL,m∈M,and there is
(6)TmL≤Treq,m∈M.Them-th device offloads the task Um to the edge server and then participates in the online collaborative training model. So the latency Tm consists of two components, the transmission latency Tmtr and the computing latency Tmcom; there are
(7)Tmtr=Dmrm,m∈M,Tmcom=FmfmE,m∈M,Tm=Tmtr+Tmcom=Dmrm+FmfmE,m∈M,and there is(8)Tm≤Treq,m∈M.The transmitted data raterm for the m-th device offloaded the task Um is represented as
(9)rm=wmBlog21+Pmtrhmσ2,m∈M,where B represents the channel bandwidth, Pmtr represents the transmission power of the m-th device, hm represents the channel gain, and σ2 represents the noise power. wm represents the proportion of subcarriers assigned to the m-th device by the edge server [1]. J represents the set of devices that perform task offloading.
(10)W=w1,⋯,wm,⋯wJ,m∈J,J≤M,where the allocated subcarrier ratio takes a real number in the range of 0 to 1.
(11)wm∈0,1,and in which the sum of the subcarriers ratios allocated by the edge server to the end devices participating in the offloading task cannot exceed 1.
(12)∑m=1Jwm≤1.In this framework, considering theedge-end online collaborative training model time is affected by the offloading latency of each end device, the slowest end device will limit the total latency, leading to the “dropout effect” problem [14]. To reduce the latency, we can allocate more subcarriers to the end device that is originally slow to reduce their transmission latency. For better online collaborative training, it is better if the transmission latency of each device is as close as possible. The relevant constraints are as follows.
(13)minmaxTmtrm∈J.Therefore, the final transmission latencyTw of the end devices participating in the edge-end collaborative training should take the maximum value of the transmission latency of the offloaded devices,
(14)Tw=maxm∈JTmtr.Then, the latencyTmn′ of the m-th device participating in the edge-end online collaborative training to complete the task Um processing after the correction is represented as
(15)Tm′=Tw+Tmcom,m∈M.The total latency is(16)T=∑m=1M1−amTmL+amTm′,m∈M.


### 4.2. Energy Model

If them-th device processes the task Um on the end device, then the energy consumption EmL of the device is represented as
(17)EmL=kmfmLvm,m∈M,where km represents the effective switched capacitance and vm represents a positive constant [1].Them-th device offloads the task to the edge server and then participates in the edge-end online collaborative optimization training. The energy consumption of the m-th device is mainly related to the transmission energy consumption, there is that,
(18)Em=pmtrDmrm,m∈M.The total energy consumption of the device is(19)E=∑m=1M1−amEmL+amEm,m∈M.


### 4.3. Problem Formulation

Since each edge server allocates resources individually, here we only analyze the online collaborative training model of a certain edge server. Specifically, the resource allocation is mainly to determine the offloading strategy and bandwidth allocation, so we describe the offloading problem and bandwidth allocation problem as a problem of minimizing the sum of cost weights. For a given offload strategy vectoram and uplink bandwidth allocation vector wm, we define the weighted sum of costs during edge-end collaborative training as
(20)O=αE+1−αT.In which,(21)α∈0,1,where α represents the weighting factor, and we can adjust α to achieve the focus of resource allocation. That is, if the task is latency-sensitive, then increase the value of 1−α, and if the focus is more on device energy consumption issues, then increase the value of α.Assuming thatA=a1,⋯,am,⋯,aJ represents the offloading strategy vector, we describe the problem as a problem of minimizing the sum of cost weights [14–17], in which
(22)P1:minA,WO,(23a)s.t.C1:am=0,1,(23b)C2:J≤M,(23c)C3:∑m=1Jwm≤1,(23d)C4:0≤wm≤1.The constraints inP1 are explained as follows: Constraint (23a) gives the binary choice of the offloading strategy. Constraint (23b) states that the number of devices participating in offloading cannot exceed the total number of devices. Constraint (23c) states that the allocated bandwidth cannot exceed the total bandwidth due to fading or loss in practice. Constraint (23d) gives a range of bandwidth allocation ratios for each device.P1 is a mixed integer nonlinear program (MINLP). P1 can be decomposed into multiple subproblems with separated objective and constraints. We can employ the Tammer decomposition method [18] to transform the original problem with high complexity into an equivalent master problem and a subproblem. P1 can be transformed into the equivalent problem P2:
(24)P2:minWminAO,s.t.C1‐C4.Therefore, solving the problem ofP2 is equivalent to solving the following task offloading problem P2.1:
(25)P2.1:minAJ∗A,s.t.C1‐C2.where J∗A is the suboptimal value function corresponding to the bandwidth allocation problem P2.2, written as
(26)P2.2:J∗A=minWO,s.t.C2‐C4.Note that the decomposition from problem ofP1 to problems of P2.1 and P2.2 does not change the optimality of the solution [18].We refer to the algorithm and related parameters in [19] to solve the suboptimal solution of problem P2. And based on this strategy, we further study the training and inference performance of deep learning models by implementing model transfer through TL, in order to verify the performance of the proposed HMECSF-TL framework in terms of cost savings and in terms of image classification accuracy and training time.


## 4.1. Latency Model

If them-th device Dm processes the task Um on the device side, then the latency only involves the calculated latency TmL which is represented as
(5)TmL=FmfmL,m∈M,and there is
(6)TmL≤Treq,m∈M.Them-th device offloads the task Um to the edge server and then participates in the online collaborative training model. So the latency Tm consists of two components, the transmission latency Tmtr and the computing latency Tmcom; there are
(7)Tmtr=Dmrm,m∈M,Tmcom=FmfmE,m∈M,Tm=Tmtr+Tmcom=Dmrm+FmfmE,m∈M,and there is(8)Tm≤Treq,m∈M.The transmitted data raterm for the m-th device offloaded the task Um is represented as
(9)rm=wmBlog21+Pmtrhmσ2,m∈M,where B represents the channel bandwidth, Pmtr represents the transmission power of the m-th device, hm represents the channel gain, and σ2 represents the noise power. wm represents the proportion of subcarriers assigned to the m-th device by the edge server [1]. J represents the set of devices that perform task offloading.
(10)W=w1,⋯,wm,⋯wJ,m∈J,J≤M,where the allocated subcarrier ratio takes a real number in the range of 0 to 1.
(11)wm∈0,1,and in which the sum of the subcarriers ratios allocated by the edge server to the end devices participating in the offloading task cannot exceed 1.
(12)∑m=1Jwm≤1.In this framework, considering theedge-end online collaborative training model time is affected by the offloading latency of each end device, the slowest end device will limit the total latency, leading to the “dropout effect” problem [14]. To reduce the latency, we can allocate more subcarriers to the end device that is originally slow to reduce their transmission latency. For better online collaborative training, it is better if the transmission latency of each device is as close as possible. The relevant constraints are as follows.
(13)minmaxTmtrm∈J.Therefore, the final transmission latencyTw of the end devices participating in the edge-end collaborative training should take the maximum value of the transmission latency of the offloaded devices,
(14)Tw=maxm∈JTmtr.Then, the latencyTmn′ of the m-th device participating in the edge-end online collaborative training to complete the task Um processing after the correction is represented as
(15)Tm′=Tw+Tmcom,m∈M.The total latency is(16)T=∑m=1M1−amTmL+amTm′,m∈M.


## 4.2. Energy Model

If them-th device processes the task Um on the end device, then the energy consumption EmL of the device is represented as
(17)EmL=kmfmLvm,m∈M,where km represents the effective switched capacitance and vm represents a positive constant [1].Them-th device offloads the task to the edge server and then participates in the edge-end online collaborative optimization training. The energy consumption of the m-th device is mainly related to the transmission energy consumption, there is that,
(18)Em=pmtrDmrm,m∈M.The total energy consumption of the device is(19)E=∑m=1M1−amEmL+amEm,m∈M.


## 4.3. Problem Formulation

Since each edge server allocates resources individually, here we only analyze the online collaborative training model of a certain edge server. Specifically, the resource allocation is mainly to determine the offloading strategy and bandwidth allocation, so we describe the offloading problem and bandwidth allocation problem as a problem of minimizing the sum of cost weights. For a given offload strategy vectoram and uplink bandwidth allocation vector wm, we define the weighted sum of costs during edge-end collaborative training as
(20)O=αE+1−αT.In which,(21)α∈0,1,where α represents the weighting factor, and we can adjust α to achieve the focus of resource allocation. That is, if the task is latency-sensitive, then increase the value of 1−α, and if the focus is more on device energy consumption issues, then increase the value of α.Assuming thatA=a1,⋯,am,⋯,aJ represents the offloading strategy vector, we describe the problem as a problem of minimizing the sum of cost weights [14–17], in which
(22)P1:minA,WO,(23a)s.t.C1:am=0,1,(23b)C2:J≤M,(23c)C3:∑m=1Jwm≤1,(23d)C4:0≤wm≤1.The constraints inP1 are explained as follows: Constraint (23a) gives the binary choice of the offloading strategy. Constraint (23b) states that the number of devices participating in offloading cannot exceed the total number of devices. Constraint (23c) states that the allocated bandwidth cannot exceed the total bandwidth due to fading or loss in practice. Constraint (23d) gives a range of bandwidth allocation ratios for each device.P1 is a mixed integer nonlinear program (MINLP). P1 can be decomposed into multiple subproblems with separated objective and constraints. We can employ the Tammer decomposition method [18] to transform the original problem with high complexity into an equivalent master problem and a subproblem. P1 can be transformed into the equivalent problem P2:
(24)P2:minWminAO,s.t.C1‐C4.Therefore, solving the problem ofP2 is equivalent to solving the following task offloading problem P2.1:
(25)P2.1:minAJ∗A,s.t.C1‐C2.where J∗A is the suboptimal value function corresponding to the bandwidth allocation problem P2.2, written as
(26)P2.2:J∗A=minWO,s.t.C2‐C4.Note that the decomposition from problem ofP1 to problems of P2.1 and P2.2 does not change the optimality of the solution [18].We refer to the algorithm and related parameters in [19] to solve the suboptimal solution of problem P2. And based on this strategy, we further study the training and inference performance of deep learning models by implementing model transfer through TL, in order to verify the performance of the proposed HMECSF-TL framework in terms of cost savings and in terms of image classification accuracy and training time.


## 5. Problem Formulation of Image Classification

The image classification task based onHMECSF-TL framework can not only improve the accuracy of image classification, but also improve the speed of image classification. This paper completes image classification based on model transfer through TL and achieves the purpose of efficient image classification by QoC-driven.


### 5.1. Convolutional Neural Network

CNN is a typical feedforward neural network for presentation learning and an influential deep learning framework for high-dimensional big data processing [3]. We first design a traditional CNN model, as shown in Figure 3.Figure 3
Illustrations of convolutional neural network


### 5.2. Transfer Learning

The general model requires massive data to train its classifier to achieve high accuracy, but requires massive data to be provided to the personalized models of the end devices or to the local model of the edge server, which is obviously not feasible. To localize the general model and reduce the requirement of training samples,TL is increasingly adopted by model training research [3]. TL can tackle the problem with few additional training samples. This is achieved by transferring the knowledge from the old task into the new task, as shown in Figure 4. TL includes model transfer, feature transfer, relationship transfer, and sample transfer [8]. In HMECSF-TL framework, model transfer is used to transfer the parameters of the general model trained with a large number of high-quality data samples to the local model, which uses local data samples for further personalized training.Figure 4
Illustrations of transfer learning.As shown in Figure4, the parameters of the deep learning algorithm trained on the cloud and on the edge nodes are transferred to the lower layers step by step, and the model in the target domain is only retrained with a limited data samples for the fully connected layer without training from scratch, which can significantly shorten the training time. In HMECSF-TL framework, the source domain is the general model, and the target domain includes both the local model and the personalized model. Since the quality and quantity of data samples available on the cloud are higher than those in the target domain, the cloud has enough knowledge to perform more finer-grained image classification to better perform tasks such as image classification.Although the traditionalCNN model we designed earlier has realized the function of image classification, it is not satisfactory through experimental verification. AlexNet is a convolutional neural network that is 8 layers deep. The pretrained network can classify images into 1000 object categories. As a result, the network has learned rich feature representations for a wide range of images. So we designed an improved Transferred AlexNet CNN model using a pretrained “AlexNet” though TL under the HMECSF-TL framework, as shown in Figure 5.Figure 5
Illustrations of Transferred AlexNet CNNAnd the above two neural network models are compared to verify whether the image classification performance of theTransferred AlexNet CNN model under the HMECSF-TL framework is better.


## 5.1. Convolutional Neural Network

CNN is a typical feedforward neural network for presentation learning and an influential deep learning framework for high-dimensional big data processing [3]. We first design a traditional CNN model, as shown in Figure 3.Figure 3
Illustrations of convolutional neural network


## 5.2. Transfer Learning

The general model requires massive data to train its classifier to achieve high accuracy, but requires massive data to be provided to the personalized models of the end devices or to the local model of the edge server, which is obviously not feasible. To localize the general model and reduce the requirement of training samples,TL is increasingly adopted by model training research [3]. TL can tackle the problem with few additional training samples. This is achieved by transferring the knowledge from the old task into the new task, as shown in Figure 4. TL includes model transfer, feature transfer, relationship transfer, and sample transfer [8]. In HMECSF-TL framework, model transfer is used to transfer the parameters of the general model trained with a large number of high-quality data samples to the local model, which uses local data samples for further personalized training.Figure 4
Illustrations of transfer learning.As shown in Figure4, the parameters of the deep learning algorithm trained on the cloud and on the edge nodes are transferred to the lower layers step by step, and the model in the target domain is only retrained with a limited data samples for the fully connected layer without training from scratch, which can significantly shorten the training time. In HMECSF-TL framework, the source domain is the general model, and the target domain includes both the local model and the personalized model. Since the quality and quantity of data samples available on the cloud are higher than those in the target domain, the cloud has enough knowledge to perform more finer-grained image classification to better perform tasks such as image classification.Although the traditionalCNN model we designed earlier has realized the function of image classification, it is not satisfactory through experimental verification. AlexNet is a convolutional neural network that is 8 layers deep. The pretrained network can classify images into 1000 object categories. As a result, the network has learned rich feature representations for a wide range of images. So we designed an improved Transferred AlexNet CNN model using a pretrained “AlexNet” though TL under the HMECSF-TL framework, as shown in Figure 5.Figure 5
Illustrations of Transferred AlexNet CNNAnd the above two neural network models are compared to verify whether the image classification performance of theTransferred AlexNet CNN model under the HMECSF-TL framework is better.


## 6. Evaluation Results

### 6.1. Datasets and Simulation Settings

In this paper, we use the dataset disclosed in [20], which contains 5932 images of contaminated rice leaves, involving four types of rice leaf diseases, namely bacterial blight, blast, brown spot, and tungro. AlexNet has an image input size of 227-by-227. So we processed the images according to 227-by-227 to adapt to the Transferred AlexNet CNN model validation; and set the first layer in the earliest designed traditional CNN model that is the image size adjustment layer, which can convert the dataset 227-by-227 images to 224-by-224 images. We set the learning rate to 0.0001 and the validation frequency to 50 iterations.In the experiment, single processorIntel i5-8250 U CPU @1.60GHz, RAM 20.0GB, 64-bit Windows 10 OS, and MATLAB R2020a were used for simulation experiments.


### 6.2. Performance Evaluation

We performedge-end collaborative training on two different CNN with the same initialization parameters to complete image classification according to the allocation strategy obtained by minimizing the weighted sum of costs. We conduct experimental verification on 400 images, 800 images, 1600 images, and 5932 images, respectively. The performance of the Transferred AlexNet CNN model is verified by experiments, and the performance of our proposed HMECSF-TL is evaluated.As shown in Figure6, the HMECSF-TL framework can effectively improve the image classification accuracy. Specifically, for image classification of 400 images, the max validation accuracy of the traditional CNN model is only 61.67%, while the max validation accuracy of the Transferred AlexNet CNN model is 79.16%. As the number of images increases, the max validation accuracy of both models improves. When 5932 images are processed, the max validation accuracy of traditional CNN model rises to 91.4%, while the max validation accuracy of the Transferred AlexNet CNN model is 99.66%. The max validation accuracy of the Transferred AlexNet CNN model based on the HMECSF-TL framework is consistently higher than that of the traditional CNN model.Figure 6
Max validation accuracy.As shown in Figure7, the Transferred AlexNet CNN model based on the HMECSF-TL framework converges faster than the traditional CNN model, and the loss of the Transferred AlexNet CNN model is smaller after convergence.Figure 7
Loss.
(a)(b)(c)(d)As shown in Figure8, the classification accuracy was evaluated on the validation set. We use the confusion matrix in the form of N rows and N columns to express the accuracy evaluation. These accuracy indicators reflect the accuracy of image classification from different aspects. The confusion matrix is calculated by comparing the position and classification of each measured pixel with the corresponding position and classification in the classified image. The resulting confusion matrix gives us additional insights on which categories are misclassified more frequently by the model.Figure 8
Confusion matrix of Transferred AlexNetCNN.
(a)(b)(c)(d)As shown in Figure9, the HMECSF-TL framework not only effectively improves the image classification accuracy, but also improves the image classification speed. Specifically, for image classification of 400 images, the processing time of the traditional CNN model is 7 minutes and 56 seconds, while the processing time of the Transferred AlexNet CNN model is 7 minutes and 7 seconds. As the number of images increases, the time required for the two models increases. When processing 5932 images, the processing time of the traditional CNN model is 148 minutes and 13 seconds, while the processing time of the Transferred AlexNet CNN model is only 102 minutes and 22 seconds. The processing time of the Transferred AlexNet CNN model based on the HMECSF-TL framework is always lower than that of the conventional CNN.Figure 9
Processing time.


## 6.1. Datasets and Simulation Settings

In this paper, we use the dataset disclosed in [20], which contains 5932 images of contaminated rice leaves, involving four types of rice leaf diseases, namely bacterial blight, blast, brown spot, and tungro. AlexNet has an image input size of 227-by-227. So we processed the images according to 227-by-227 to adapt to the Transferred AlexNet CNN model validation; and set the first layer in the earliest designed traditional CNN model that is the image size adjustment layer, which can convert the dataset 227-by-227 images to 224-by-224 images. We set the learning rate to 0.0001 and the validation frequency to 50 iterations.In the experiment, single processorIntel i5-8250 U CPU @1.60GHz, RAM 20.0GB, 64-bit Windows 10 OS, and MATLAB R2020a were used for simulation experiments.


## 6.2. Performance Evaluation

We performedge-end collaborative training on two different CNN with the same initialization parameters to complete image classification according to the allocation strategy obtained by minimizing the weighted sum of costs. We conduct experimental verification on 400 images, 800 images, 1600 images, and 5932 images, respectively. The performance of the Transferred AlexNet CNN model is verified by experiments, and the performance of our proposed HMECSF-TL is evaluated.As shown in Figure6, the HMECSF-TL framework can effectively improve the image classification accuracy. Specifically, for image classification of 400 images, the max validation accuracy of the traditional CNN model is only 61.67%, while the max validation accuracy of the Transferred AlexNet CNN model is 79.16%. As the number of images increases, the max validation accuracy of both models improves. When 5932 images are processed, the max validation accuracy of traditional CNN model rises to 91.4%, while the max validation accuracy of the Transferred AlexNet CNN model is 99.66%. The max validation accuracy of the Transferred AlexNet CNN model based on the HMECSF-TL framework is consistently higher than that of the traditional CNN model.Figure 6
Max validation accuracy.As shown in Figure7, the Transferred AlexNet CNN model based on the HMECSF-TL framework converges faster than the traditional CNN model, and the loss of the Transferred AlexNet CNN model is smaller after convergence.Figure 7
Loss.
(a)(b)(c)(d)As shown in Figure8, the classification accuracy was evaluated on the validation set. We use the confusion matrix in the form of N rows and N columns to express the accuracy evaluation. These accuracy indicators reflect the accuracy of image classification from different aspects. The confusion matrix is calculated by comparing the position and classification of each measured pixel with the corresponding position and classification in the classified image. The resulting confusion matrix gives us additional insights on which categories are misclassified more frequently by the model.Figure 8
Confusion matrix of Transferred AlexNetCNN.
(a)(b)(c)(d)As shown in Figure9, the HMECSF-TL framework not only effectively improves the image classification accuracy, but also improves the image classification speed. Specifically, for image classification of 400 images, the processing time of the traditional CNN model is 7 minutes and 56 seconds, while the processing time of the Transferred AlexNet CNN model is 7 minutes and 7 seconds. As the number of images increases, the time required for the two models increases. When processing 5932 images, the processing time of the traditional CNN model is 148 minutes and 13 seconds, while the processing time of the Transferred AlexNet CNN model is only 102 minutes and 22 seconds. The processing time of the Transferred AlexNet CNN model based on the HMECSF-TL framework is always lower than that of the conventional CNN.Figure 9
Processing time.


## 7. Conclusion

TheHMECSF-TL framework studied in this paper improves the model training speed though TL and uses collaborative optimization to improve the accuracy of the model. We verified the effectiveness of the Transferred AlexNet CNN model inference work under the HMECSF-TL framework through simulation experiments and also verified the performance improvement of image classification under the HMECSF-TL framework. It can be seen that the proposed HMECSF-TL framework outperforms the benchmark strategy without TL in terms of reducing the model training time and improving the image classification accuracy, as well as reducing the offloading cost. In the next research, we plan to apply HMECSF-TL framework to more aspects, such as object detection and target tracking and expect the framework to play a greater role.



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*Source: 7863972-2022-08-19.xml*