# The Integrated Processing Method of Educational Information Resources Based on Edge Computing

Authors: Zhimei Lv
Journal: Journal of Electrical and Computer Engineering
 (2022)

---

## Abstract

Due to the development of information technology, the new type of smart campus integrated teaching, scientific research, office services, and many other applications, online education information resources show a blowout growth. However, huge amounts of data to the campus network carrying capacity put forward high requirements. With the help of edge computing and cloud computing platform and network, the teaching, scientific research, office services, and many other applications of big data integration mining analysis build intelligent management, teaching, scientific research, and life mode, to improve the utilization of education information resources. Improving the school management level and education quality is imperative. The research purpose of this paper is to study the integrated processing method of educational information resources based on edge computing. The research of this paper enriches the relevant optimization theory of edge computing on the one hand and provides reference for the practical integrated processing of practical educational information resources on the other hand, which has the dual significance of theory and practice.


---

## Body

## 1. Introduction

With the formulation of national standards for the overall framework of smart campus and the promulgation of a series of national education policies, smart campus has once again become the focus of scholars' research. Under the vigorous promotion of national policies, the construction of smart campus of various schools has achieved rapid development [1]. Basic platforms for smart campus transactions, such as cloud computing and big data centers, have sprung up, but at the same time, they have also exposed many problems. For example, the cloud computing platform has low resource utilization rate and excessive energy consumption, the big data center has high computing pressure and frequent safety accidents, and the problems of high terminal equipment delay and poor user experience are gradually prominent [2]. The centralized big data processing centered on the cloud computing model cannot efficiently process the data generated by edge devices. In the era of edge-based big data processing, network edge devices will generate massive amounts of real-time data; and these edge devices will deploy edge computing platforms that support real-time data processing to provide users with many services or functional interfaces, which users can obtain by calling these interfaces. The required edge computing services can effectively reduce user latency. Therefore, it is urgent to reconstruct the integrated processing method of educational information resources in the background of big data and become an important problem to be solved.This paper mainly carries out the following research: Section1 introduces the research background, research necessity, and the paper chapter arrangement; Section 2 mainly introduces the current status quo of edge computing and its application and analyzes the feasibility of applying edge computing in the field of educational information resources; Section 3 mainly studies the application framework of edge computing in the field of educational information resources. A data compression algorithm and an asynchronous federated learning mechanism suitable for edge learning are proposed, to improve the integration of educational information resources; Section 4 mainly selects the learning resources on an online learning website as an experimental dataset for empirical analysis. This paper is verified by the constructed edge calculations, analyzing the experimental results and the errors, with an arrival at a conclusion. Experiments show that the integrated processing method of educational information resources based on edge computing can greatly improve the compression rate of data. By using the asynchronous learning mechanism to build a “cloud-end” dictionary, it effectively reduces the pressure of data interaction between “cloud-end” and makes the efficiency of information resources significantly improved. The research purpose of this paper is to study the integrated processing method of educational information resources based on edge computing. The research of this paper enriches the relevant optimization theory of edge computing on the one hand and provides reference for the practical integrated processing of practical educational information resources on the other hand, which has the dual significance of theory and practice.


## 2. State of the Art

In the era of big data, the amount of data generated every day increases rapidly, while the data in the background of applications such as the Internet of Things is geographically dispersed, and it puts forward higher requirements for response time and security. Although cloud computing provides an efficient computing platform for big data processing, the current growth rate of network bandwidth is far behind the growth speed of data. In this context, edge computing has emerged and has attracted wide attention from researchers in the past two years. Edge computing refers to the network edge computing and storage resources, the edge of the network, and the data center, both from the geographical distance or network distance closer to the user. Edge computing is using these resources in the network edge to provide services for the user, enabling the application to process data near the data source [3].Edge computing provides services near data sources, giving it a huge advantage in many mobile and Internet of Things applications. Tang et al. [4] propose a big data analysis framework based on smart city, which has good results on processing geographically widely distributed data. Wayne State University has built an edge computing platform on Geni Racks and has deployed three applications: real-time 3D campus map, vehicle status detection, and Internet of vehicles simulation. The 3D campus map integrates the campus surveillance video with the video data of the driving vehicles, which can be enhanced into a real-time 3D map after processing, and the campus security personnel can seamlessly monitor the campus status [5]. Chang et al. [6] put forward a video monitoring framework based on edge calculation, which can be in the front or near the location of the video content, to detect camera fault, content error, and dynamic adjustment of video quality according to the content, and near the edge equipment real-time to video processing, and fully guarantee public security. Ning et al. [7] put forward multicar sensor information fusion and timeliness sharing problem, based on perception-communication-computing fusion intelligent car networking methods and solutions; help to improve the collaborative environment perception; through the mobile edge computing technology to reduce workshop perception information transmission load, improve the car collaborative information fusion and processing efficiency; and finally realize based on multicar intelligent collaborative safe automatic driving. Product description: Song et al. and Li [8, 9], respectively, put forward a smart home control system based on mobile edge computing. On the premise of saving computing resources and improving the utilization rate of computing resources, they apply MEC to the smart home environment and build a smart home MEC system based on personal computer platform. With limited computing resources, the system can provide higher service quality and better scalability than the cloud computing system alone, and it is easy to deploy and manage. Ren Yanguang designed a set of underwater image processing system based on edge computing platform by combining edge computing platform, underwater image enhancement technology and underwater target detection algorithm, which realized real-time detection of underwater multitargets [10]. Jia et al. used a combination of mobile edge computing (MEC) and face mask recognition algorithm to deploy the video image data processing tasks of the video acquisition terminal on the edge cloud to realize the real-time video image processing. The information is processed locally, which greatly reduces the pressure on network bandwidth [11].To sum up, edge computing is especially suitable for the scattered distribution of data and devices and the transmission of more information resources, which is exactly the characteristic of educational information resources. The integrated processing method of designing educational information resources by using edge computing can effectively improve the utilization rate of educational information resources. The advantages of edge-based processing method mainly include three aspects: (1) complete data processing near the data source, reduce transmission delay, and improve data processing efficiency; (2) edge computing and storage cost is lower compared with the cloud center; (3) there is low dependence on the cloud, which can reduce the data transmission error rate and single point failure rate. Although techniques based on edge computing have been widely used, several problems remain. Currently about edge computing [12], the research mainly focuses on the management and control of single-subject resources and has not involved the management of multisubject resources. Edge computing uses many computational nodes, but the computing resources of nodes are limited. How to weigh the value and timeliness of extracted information is also a key problem [13].As can be seen from the literature, most scholars use edge computing saving computing resources and improve the utilization of computing resources and achieved certain results, but at present, how to use the edge computing design education information resources integration processing research is relatively little; there is a blank, so this paper adds it.


## 3. Methodology

### 3.1. Framework Construction of the Integrated Processing System

According to the related relationships of campus affairs such as teaching environment construction, teaching resource service, and teaching and scientific research analysis in colleges and universities explained by Soldatos et al. [14], in order to ensure the coordination of the computing power of the integrated processing platform, the system should be deployed as a whole in terms of “end, edge and cloud.” In the auxiliary system of art painting teaching course based on Bian Yun and mobile auxiliary equipment, the construction of Bian Yun is set in the school. The terminal provides support. In view of this, the requirement of the auxiliary teaching system for the mobile auxiliary terminal is not high, as long as it can meet the basic drawing and video requirements. Figure 1 shows the auxiliary system of art painting teaching course based on edge cloud and mobile auxiliary equipment.Figure 1
Integrated processing platform framework based on edge computing.According to the overall deployment of computing of “end, edge and cloud,” the integrated processing method is composed of three types of computing modules: ① campus computing. The intelligent terminal equipment with independent computing power can preprocess the locally collected data, improve the overall data processing capacity of the platform, and reduce the amount of network data transmission. Intelligent terminal facilities relate to the edge nodes through the campus network to realize the data communication between “ends and edges.” The campus terminal calculates the multimodal field data such as teacher and student information, teaching situation, and campus security status collected by the terminal to the edge nodes of the campus in real time. ② Campus edge computing: the edge node facilities with integrated processing are connected to the terminal facilities and cloud resource facilities, respectively, through the campus network, becoming a transmission bridge between “end and cloud.” Edge nodes are the core resources for the collaboration of integrated processing methods. They deploy local independent applications, conduct real-time processing of the collected data, support AI computing and data cache, and provide image rendering, video caching, data filtering, and other services. Edge nodes are responsible for receiving and processing the calculated and collected campus ecological data and providing time-sensitive services such as real-time perception and optimization of the data, for example, perception of external light control curtain opening and closing, perception of whether there is a class control door lock opening and closing, perception of course content push course video, perception of learning state evaluation of classroom effect, and other services, and it forwards the campus affairs requiring long-term decision to the campus cloud resources. Edge computing has the characteristics of lightweight and low energy consumption, which can be widely deployed in the field of campus affairs and fully cooperate with campus cloud computing for education management, campus management, and other affairs. ③ Campus cloud computing: cloud resource facilities with integrated processing methods relate to edge nodes through the campus network to realize the data communication between “edge and cloud.” Campus cloud computing is responsible for receiving the data flow forwarded by edge nodes and carrying out long-term data mining, machine learning, and other intelligent computing for time-insensitive data, to provide intelligent decision-making reference services for campus transaction management, to realize the comprehensive application and optimization of educational information resources. For example, with the change of seasons, provide the dynamic decision reference of campus power supply time, with the optimization of campus personnel, assets, and scientific research, with the proportion of teachers and disciplines. At the same time, the platform operation and maintenance management terminal can also be set up in the core network to ensure the safe operation of the main system body.Building based on edge computing education information resources integrated processing method mainly depends on the processing system, including the cloud online data compression and distribution, as well as local data processing and upload, relying on their own computing resources and storage resources, through asynchronous learning mechanism to build a global complete dictionary and common support education information resources integrated processing, realizing the effective use of information.


### 3.2. Data Compression Method of Educational Information Resources Based on Edge Computing

Thanks to the rapid development of Internet technology and the landing of 5G technology, online education resources have shown an exponential growth. However, due to the limitation of carrying capacity of campus network and the performance of mobile devices, massive online education information resources cannot be extended to everyone; let alone meeting the needs of users with low latency and low power consumption. In order to improve the utilization of educational resources, the effective integration of educational information resources is essential. At the same time, in order to effectively make educational resources sink to the end, it is imperative to compress and optimize educational information resources.The typical transmission way of campus education information is shown in Figure2. The cloud and the end have the ability of information processing. Among them, the cloud data mainly comes from the information uploaded by the network and the end, while the end data mainly comes from the cloud distribution and the terminal device collection. However, the large amount of data, slow transmission speed, and low storage efficiency caused by the storage and computing of large amounts of cloud data have successively led to problems such as resource consumption and storage space occupation [15]. Traditional data compression algorithms are usually carried out at one end and cannot meet the actual needs of “end, edge, and cloud” in the case of edge computing.Figure 2
Data interaction mode of an integrated processing method based on edge computing.Cloud computing technology can be used when there is a large amount of data to be analyzed [16]. This technology has the advantages of maintenance-free computing hardware, associated software, and no need to store data locally, but the disadvantage is the slow response time, which is due to the long distance between other terminals and the cloud platform [17]. An innovative way of using edge computing is now introduced. The proposed algorithm is closer to other terminals, which can solve the problem of slow response time caused by cloud computing due to distance, while the broadband needed between data and storage also decreases. Therefore, the introduction of edge computing with cloud computing work can reduce the network latency and improve the system performance.Based on the cloud edge collaborative framework as shown in Figure3, collect online learning resources, on the cloud server edge computing CS-SOMP joint reconstruction algorithm reconstruction of sparse dictionary atoms and measurement, and then for online education information resources data compression storage, and establish a complete sparse dictionary, using the edge server to the cloud server send resource adjustment, collect resources, and then send to the cloud server [18].Figure 3
Cloud edge collaboration framework based on edge computing.Under the cloud-side collaborative architecture, when the CS-SOMP joint reconstruction algorithm is used to compress and collect the data ofs nodes together, each data uses the same dictionary atom, and the data length of each node is set to n, and τ is the number of uploaded dictionary atoms. Then it can be expressed as (1)Ym∗s,Dτ∗n=DCS_SOPMPXn∗s,ψm∗n,ϕn∗n,τ,SNRdef.Among them, the valueYm∗s is measured for each node, Dτ∗n is the dictionary atoms, and Xn∗s is the original signal of each node. The numbers of measurements m and dictionary atoms τ uploaded to the cloud decrease with the length of the matrix and the number of dictionary atoms uploaded to the cloud. The establishment of the complete dictionary Dτ∗n requires the cloud integration of dictionary atoms uploaded at each edge to ensure a rapid and accurate call of cloud data, which k represents the total number of atoms. The sparse representation coefficients θn∗s are described as(2)θn∗s=SOMPYm∗s,Dτ∗n,ψm∗n.UseXn∗s′ to represent the original signal, and restore the original signal calculated as (3)Xn∗s′=realϕn∗nθn∗s.The compressed storage of data is completed by establishing a complete dictionary in the cloud. At this time, only the measured values need to be uploaded on each edge, greatly reducing the storage space in the cloud. The specific process of building a complete dictionary is as follows:(i)
For dictionary atomsdi, Dk is the k atom in the cloud initial sparse dictionary Dτ∗n, and their correlation is expressed as ri,k; there it can be described as(4)ri,k=covdi,DkvardivarDk.If the dictionary atomsdi uploaded to the cloud have a weak overall correlation with the cloud sparse dictionary Dτ∗n, each one ri,k is lower than a certain threshold, and the dictionary atom is expanded into the cloud sparse dictionary.(ii)
The overcomplete sparse dictionary (5) consists of the uploaded dictionary atoms, and the correlation between each dictionary atoms decreases by regularization. The processing process is shown in (6):(5)Dk∗n=d1,d2,d3,…..,dk,,(6)dk′=dk−dk,d1′d1′,d1′d1′−dk,d2′d2′,d2′d2′−⋯−dk,dk−1′dk−1′,dk−1′dk−1′.(iii)
Dictionary atoms are updated by normalizing overcomplete dictionaries. The normalization process is shown in(7)dk=dk′dk′.(iv)
In the uploaded measurements, the original data is restored in combination with an overcomplete sparse dictionary, thus confirming the possibility of recovering the stored data. The corresponding sparse coefficient is obtained at each nodeθj,j∈1,s, and the compressed storage of the data is completed by taking the measurement value of the data at each node as the stored data [19].


### 3.3. Asynchronous Federated Learning Mechanism Based on Edge Computing

Section design of “cloud-end” data compression method can effectively improve the integration of education information resources processing degree, but because the education information resources contain all kinds of the campus sensitive data, it may even involve serious privacy problems: there are tens of thousands of people’s photos, video is centralized collection, abd users can neither delete the data nor control the use of data [20]. Although edge computing solves this problem to some extent, the update of dictionary atoms cannot be synchronized between different universities due to different computing resources and requirements. The mechanism of asynchronous federated learning can effectively solve this problem.Federated learning has the meaning of “joint learning”—multiple devices jointly train predictive models. Federal learning can be built on edge devices (such as smart phones and video surveillance devices). Each edge node independently trains the machine learning model locally, and the global model is optimized and merged through the central server. A general description of federated learning was given by Konen et al. [21]. The asynchronous federated learning mechanism can enable schools to generate dictionaries on their own local data and then update the global complete dictionary, providing a freer learning method for edge learning devices and reducing the accuracy loss caused by high degrees of freedom.Figure4 shows the typical federated learning mechanism, which consists of one parameter (complete dictionary) server and multiple edge nodes. The parameter server is responsible for collecting the dictionary uploaded by each participating node and updating the global complete dictionary according to the optimization algorithm; the participating nodes independently generate the dictionary for the locally owned sensitive dataset. After each round of learning, the nodes upload the calculated dictionary data to the parameter server, which summarizes and updates the global dictionary. The node then downloads the updated global dictionary from the parameter server, overrides the local dictionary, and performs the next round of iteration. During the whole learning process, the nodes only communicate with the parameter server, and the nodes cannot obtain any information about the other nodes except for the jointly maintained global dictionary, ensuring the confidentiality of private data.Figure 4
Schematic diagram of the federated learning mechanism.The important thing of the asynchronous federated learning mechanism for edge network computing is to configure the weights, which are mainly divided into two parts: sample weight and parameter weight. The sample weight is determined by the proportion of the number of samples to the total number of samples, while the parameter weight is affected by the temporal proximity of the current dictionary and the current global dictionary. Sample weights can be expressed as(8)βs=DiD,where D=∑j=0nDj; this is the total number of samples for all nodes. Each node has other nodes interspersed in the time from downloading the latest global dictionary to upload the updated global dictionary. Therefore, the dictionary uploaded by the local node has certain obsolescence, and the parameter obsolescence is the number of nodes that have updated the dictionary during that period and can be described as(9)μ=Iupload−Idownload.In order to make the node with older age have smaller parameter weight, and the decay process is relatively gentle, the exponential function with the base number less than 1 is selected to construct the decay function of the parameter weight, as(10)βp=∂1/m−1μ−1,where m is the number of all end nodes. For double weight correction, the dictionary should be described as (11)dk″=dk∗βs∗βp.During asynchronous learning, the dictionary submitted by edge nodes is modified by double weights on the parameter server to participate in the subsequent data compression process.


## 3.1. Framework Construction of the Integrated Processing System

According to the related relationships of campus affairs such as teaching environment construction, teaching resource service, and teaching and scientific research analysis in colleges and universities explained by Soldatos et al. [14], in order to ensure the coordination of the computing power of the integrated processing platform, the system should be deployed as a whole in terms of “end, edge and cloud.” In the auxiliary system of art painting teaching course based on Bian Yun and mobile auxiliary equipment, the construction of Bian Yun is set in the school. The terminal provides support. In view of this, the requirement of the auxiliary teaching system for the mobile auxiliary terminal is not high, as long as it can meet the basic drawing and video requirements. Figure 1 shows the auxiliary system of art painting teaching course based on edge cloud and mobile auxiliary equipment.Figure 1
Integrated processing platform framework based on edge computing.According to the overall deployment of computing of “end, edge and cloud,” the integrated processing method is composed of three types of computing modules: ① campus computing. The intelligent terminal equipment with independent computing power can preprocess the locally collected data, improve the overall data processing capacity of the platform, and reduce the amount of network data transmission. Intelligent terminal facilities relate to the edge nodes through the campus network to realize the data communication between “ends and edges.” The campus terminal calculates the multimodal field data such as teacher and student information, teaching situation, and campus security status collected by the terminal to the edge nodes of the campus in real time. ② Campus edge computing: the edge node facilities with integrated processing are connected to the terminal facilities and cloud resource facilities, respectively, through the campus network, becoming a transmission bridge between “end and cloud.” Edge nodes are the core resources for the collaboration of integrated processing methods. They deploy local independent applications, conduct real-time processing of the collected data, support AI computing and data cache, and provide image rendering, video caching, data filtering, and other services. Edge nodes are responsible for receiving and processing the calculated and collected campus ecological data and providing time-sensitive services such as real-time perception and optimization of the data, for example, perception of external light control curtain opening and closing, perception of whether there is a class control door lock opening and closing, perception of course content push course video, perception of learning state evaluation of classroom effect, and other services, and it forwards the campus affairs requiring long-term decision to the campus cloud resources. Edge computing has the characteristics of lightweight and low energy consumption, which can be widely deployed in the field of campus affairs and fully cooperate with campus cloud computing for education management, campus management, and other affairs. ③ Campus cloud computing: cloud resource facilities with integrated processing methods relate to edge nodes through the campus network to realize the data communication between “edge and cloud.” Campus cloud computing is responsible for receiving the data flow forwarded by edge nodes and carrying out long-term data mining, machine learning, and other intelligent computing for time-insensitive data, to provide intelligent decision-making reference services for campus transaction management, to realize the comprehensive application and optimization of educational information resources. For example, with the change of seasons, provide the dynamic decision reference of campus power supply time, with the optimization of campus personnel, assets, and scientific research, with the proportion of teachers and disciplines. At the same time, the platform operation and maintenance management terminal can also be set up in the core network to ensure the safe operation of the main system body.Building based on edge computing education information resources integrated processing method mainly depends on the processing system, including the cloud online data compression and distribution, as well as local data processing and upload, relying on their own computing resources and storage resources, through asynchronous learning mechanism to build a global complete dictionary and common support education information resources integrated processing, realizing the effective use of information.


## 3.2. Data Compression Method of Educational Information Resources Based on Edge Computing

Thanks to the rapid development of Internet technology and the landing of 5G technology, online education resources have shown an exponential growth. However, due to the limitation of carrying capacity of campus network and the performance of mobile devices, massive online education information resources cannot be extended to everyone; let alone meeting the needs of users with low latency and low power consumption. In order to improve the utilization of educational resources, the effective integration of educational information resources is essential. At the same time, in order to effectively make educational resources sink to the end, it is imperative to compress and optimize educational information resources.The typical transmission way of campus education information is shown in Figure2. The cloud and the end have the ability of information processing. Among them, the cloud data mainly comes from the information uploaded by the network and the end, while the end data mainly comes from the cloud distribution and the terminal device collection. However, the large amount of data, slow transmission speed, and low storage efficiency caused by the storage and computing of large amounts of cloud data have successively led to problems such as resource consumption and storage space occupation [15]. Traditional data compression algorithms are usually carried out at one end and cannot meet the actual needs of “end, edge, and cloud” in the case of edge computing.Figure 2
Data interaction mode of an integrated processing method based on edge computing.Cloud computing technology can be used when there is a large amount of data to be analyzed [16]. This technology has the advantages of maintenance-free computing hardware, associated software, and no need to store data locally, but the disadvantage is the slow response time, which is due to the long distance between other terminals and the cloud platform [17]. An innovative way of using edge computing is now introduced. The proposed algorithm is closer to other terminals, which can solve the problem of slow response time caused by cloud computing due to distance, while the broadband needed between data and storage also decreases. Therefore, the introduction of edge computing with cloud computing work can reduce the network latency and improve the system performance.Based on the cloud edge collaborative framework as shown in Figure3, collect online learning resources, on the cloud server edge computing CS-SOMP joint reconstruction algorithm reconstruction of sparse dictionary atoms and measurement, and then for online education information resources data compression storage, and establish a complete sparse dictionary, using the edge server to the cloud server send resource adjustment, collect resources, and then send to the cloud server [18].Figure 3
Cloud edge collaboration framework based on edge computing.Under the cloud-side collaborative architecture, when the CS-SOMP joint reconstruction algorithm is used to compress and collect the data ofs nodes together, each data uses the same dictionary atom, and the data length of each node is set to n, and τ is the number of uploaded dictionary atoms. Then it can be expressed as (1)Ym∗s,Dτ∗n=DCS_SOPMPXn∗s,ψm∗n,ϕn∗n,τ,SNRdef.Among them, the valueYm∗s is measured for each node, Dτ∗n is the dictionary atoms, and Xn∗s is the original signal of each node. The numbers of measurements m and dictionary atoms τ uploaded to the cloud decrease with the length of the matrix and the number of dictionary atoms uploaded to the cloud. The establishment of the complete dictionary Dτ∗n requires the cloud integration of dictionary atoms uploaded at each edge to ensure a rapid and accurate call of cloud data, which k represents the total number of atoms. The sparse representation coefficients θn∗s are described as(2)θn∗s=SOMPYm∗s,Dτ∗n,ψm∗n.UseXn∗s′ to represent the original signal, and restore the original signal calculated as (3)Xn∗s′=realϕn∗nθn∗s.The compressed storage of data is completed by establishing a complete dictionary in the cloud. At this time, only the measured values need to be uploaded on each edge, greatly reducing the storage space in the cloud. The specific process of building a complete dictionary is as follows:(i)
For dictionary atomsdi, Dk is the k atom in the cloud initial sparse dictionary Dτ∗n, and their correlation is expressed as ri,k; there it can be described as(4)ri,k=covdi,DkvardivarDk.If the dictionary atomsdi uploaded to the cloud have a weak overall correlation with the cloud sparse dictionary Dτ∗n, each one ri,k is lower than a certain threshold, and the dictionary atom is expanded into the cloud sparse dictionary.(ii)
The overcomplete sparse dictionary (5) consists of the uploaded dictionary atoms, and the correlation between each dictionary atoms decreases by regularization. The processing process is shown in (6):(5)Dk∗n=d1,d2,d3,…..,dk,,(6)dk′=dk−dk,d1′d1′,d1′d1′−dk,d2′d2′,d2′d2′−⋯−dk,dk−1′dk−1′,dk−1′dk−1′.(iii)
Dictionary atoms are updated by normalizing overcomplete dictionaries. The normalization process is shown in(7)dk=dk′dk′.(iv)
In the uploaded measurements, the original data is restored in combination with an overcomplete sparse dictionary, thus confirming the possibility of recovering the stored data. The corresponding sparse coefficient is obtained at each nodeθj,j∈1,s, and the compressed storage of the data is completed by taking the measurement value of the data at each node as the stored data [19].


## 3.3. Asynchronous Federated Learning Mechanism Based on Edge Computing

Section design of “cloud-end” data compression method can effectively improve the integration of education information resources processing degree, but because the education information resources contain all kinds of the campus sensitive data, it may even involve serious privacy problems: there are tens of thousands of people’s photos, video is centralized collection, abd users can neither delete the data nor control the use of data [20]. Although edge computing solves this problem to some extent, the update of dictionary atoms cannot be synchronized between different universities due to different computing resources and requirements. The mechanism of asynchronous federated learning can effectively solve this problem.Federated learning has the meaning of “joint learning”—multiple devices jointly train predictive models. Federal learning can be built on edge devices (such as smart phones and video surveillance devices). Each edge node independently trains the machine learning model locally, and the global model is optimized and merged through the central server. A general description of federated learning was given by Konen et al. [21]. The asynchronous federated learning mechanism can enable schools to generate dictionaries on their own local data and then update the global complete dictionary, providing a freer learning method for edge learning devices and reducing the accuracy loss caused by high degrees of freedom.Figure4 shows the typical federated learning mechanism, which consists of one parameter (complete dictionary) server and multiple edge nodes. The parameter server is responsible for collecting the dictionary uploaded by each participating node and updating the global complete dictionary according to the optimization algorithm; the participating nodes independently generate the dictionary for the locally owned sensitive dataset. After each round of learning, the nodes upload the calculated dictionary data to the parameter server, which summarizes and updates the global dictionary. The node then downloads the updated global dictionary from the parameter server, overrides the local dictionary, and performs the next round of iteration. During the whole learning process, the nodes only communicate with the parameter server, and the nodes cannot obtain any information about the other nodes except for the jointly maintained global dictionary, ensuring the confidentiality of private data.Figure 4
Schematic diagram of the federated learning mechanism.The important thing of the asynchronous federated learning mechanism for edge network computing is to configure the weights, which are mainly divided into two parts: sample weight and parameter weight. The sample weight is determined by the proportion of the number of samples to the total number of samples, while the parameter weight is affected by the temporal proximity of the current dictionary and the current global dictionary. Sample weights can be expressed as(8)βs=DiD,where D=∑j=0nDj; this is the total number of samples for all nodes. Each node has other nodes interspersed in the time from downloading the latest global dictionary to upload the updated global dictionary. Therefore, the dictionary uploaded by the local node has certain obsolescence, and the parameter obsolescence is the number of nodes that have updated the dictionary during that period and can be described as(9)μ=Iupload−Idownload.In order to make the node with older age have smaller parameter weight, and the decay process is relatively gentle, the exponential function with the base number less than 1 is selected to construct the decay function of the parameter weight, as(10)βp=∂1/m−1μ−1,where m is the number of all end nodes. For double weight correction, the dictionary should be described as (11)dk″=dk∗βs∗βp.During asynchronous learning, the dictionary submitted by edge nodes is modified by double weights on the parameter server to participate in the subsequent data compression process.


## 4. Result Analysis and Discussion

### 4.1. Verification of Data Compression Method of Educational Information Resources Based on Edge Calculation

Using the resources of an online learning website, the online learning resource compression storage performance of this method is verified. When using the present method to store online learning resources, it is necessary to calculate the collected data and the corresponding sparse dictionary atoms and measurement values in the cloud and then restore them. The same as the other parameters, word sizes are 128, 256, 512, and 1024, respectively. The results of SNR changes of different size dictionaries when the sparsity and compression ratio gradually increase are shown in Figure5.Figure 5
Dictionary SNR comparison of different sizes. (a) Signal-to-noise ratio at different sparsity. (b) Signal-to-noise ratio at different compression ratio.
(a)(b)It can be seen from the figure that the SNR of different size dictionaries increases with the increase of the dictionary, and the SNR of 128-word dictionary and 256-word dictionary decreases significantly after the compression ratio reaches 20%, but the 1024 size dictionary remains stable and the higher SNR of other dictionaries, indicating that the larger the dictionary, the better the data processing performance with the method.In the process of storing online learning resources, the number of samples is also an important factor affecting dictionary learning. With a fixed dictionary size of 1024, 25, 50, 75, and 100 samples were compared, and the results are shown in Figure6.Figure 6
Training learning dictionary performance results graph for different sample numbers. (a) Signal-to-noise ratio at different sparsity. (b) Signal-to-noise ratio at different compression ratio.
(a)(b)Figure6(a) shows that, with the increase of sparsity, the SNR ratio of each dictionary increases, and the growth rate in the later period becomes smaller and gradually becomes stable. However, the larger the number of samples, the higher the signal-to-noise ratio, the faster the early rise, and the better the dictionary learning effect. In Figure 6(b), the less training samples change the compression ratio. Only 25 dictionaries have severe compression ratio of 30%, while the dictionary SNR of 100 samples remains stable with increasing compression ratio. The experimental results show that when pressing the online learning resources, the larger the number of samples, the better the training learning dictionary and the more significant the compression storage advantage.


### 4.2. Verification of the Data Compression Effect under the Asynchronous Learning Mechanism

To verify the effect of the asynchronous federal learning mechanism on the integrated processing of educational information resources, 10 sets of controlled experiments were set up to verify the recovery accuracy under different weights. First, the built global dictionary is used to compress the cloud education resources, and then the transmitted data is downloaded to the local end, and finally, the information resources are recovered at the local end.As can be seen from Figure7, although the 10 sets of experimental data fluctuate on individuals, they generally show a trend of continuous growth; that is to say, the experimental data show a trend of increasing accuracy with the increase of weight. At the beginning, the growth trend is large; that is, the weight has a large impact on the experimental data, but when the weight increases to a certain extent, the impact on the accuracy is small, and there are fluctuations, because the generated dictionary has achieved the best effect and cannot continue to be optimized.Figure 7
Training learning dictionary performance results graph for different sample numbers. (a) Train set. (b) Test set.
(a)(b)


## 4.1. Verification of Data Compression Method of Educational Information Resources Based on Edge Calculation

Using the resources of an online learning website, the online learning resource compression storage performance of this method is verified. When using the present method to store online learning resources, it is necessary to calculate the collected data and the corresponding sparse dictionary atoms and measurement values in the cloud and then restore them. The same as the other parameters, word sizes are 128, 256, 512, and 1024, respectively. The results of SNR changes of different size dictionaries when the sparsity and compression ratio gradually increase are shown in Figure5.Figure 5
Dictionary SNR comparison of different sizes. (a) Signal-to-noise ratio at different sparsity. (b) Signal-to-noise ratio at different compression ratio.
(a)(b)It can be seen from the figure that the SNR of different size dictionaries increases with the increase of the dictionary, and the SNR of 128-word dictionary and 256-word dictionary decreases significantly after the compression ratio reaches 20%, but the 1024 size dictionary remains stable and the higher SNR of other dictionaries, indicating that the larger the dictionary, the better the data processing performance with the method.In the process of storing online learning resources, the number of samples is also an important factor affecting dictionary learning. With a fixed dictionary size of 1024, 25, 50, 75, and 100 samples were compared, and the results are shown in Figure6.Figure 6
Training learning dictionary performance results graph for different sample numbers. (a) Signal-to-noise ratio at different sparsity. (b) Signal-to-noise ratio at different compression ratio.
(a)(b)Figure6(a) shows that, with the increase of sparsity, the SNR ratio of each dictionary increases, and the growth rate in the later period becomes smaller and gradually becomes stable. However, the larger the number of samples, the higher the signal-to-noise ratio, the faster the early rise, and the better the dictionary learning effect. In Figure 6(b), the less training samples change the compression ratio. Only 25 dictionaries have severe compression ratio of 30%, while the dictionary SNR of 100 samples remains stable with increasing compression ratio. The experimental results show that when pressing the online learning resources, the larger the number of samples, the better the training learning dictionary and the more significant the compression storage advantage.


## 4.2. Verification of the Data Compression Effect under the Asynchronous Learning Mechanism

To verify the effect of the asynchronous federal learning mechanism on the integrated processing of educational information resources, 10 sets of controlled experiments were set up to verify the recovery accuracy under different weights. First, the built global dictionary is used to compress the cloud education resources, and then the transmitted data is downloaded to the local end, and finally, the information resources are recovered at the local end.As can be seen from Figure7, although the 10 sets of experimental data fluctuate on individuals, they generally show a trend of continuous growth; that is to say, the experimental data show a trend of increasing accuracy with the increase of weight. At the beginning, the growth trend is large; that is, the weight has a large impact on the experimental data, but when the weight increases to a certain extent, the impact on the accuracy is small, and there are fluctuations, because the generated dictionary has achieved the best effect and cannot continue to be optimized.Figure 7
Training learning dictionary performance results graph for different sample numbers. (a) Train set. (b) Test set.
(a)(b)


## 5. Conclusions

Due to the development of information technology, the new smart campus integrates many applications such as teaching, scientific research, and office services, and the online education information resources show a blowout growth. However, massive data puts forward high requirements on the network carrying capacity of the campus. With the help of high-performance computing platforms and networks such as edge computing and cloud computing, the big data of teaching, scientific research, office services, and many other applications can be integrated, mined, analyzed, and constructed. It is imperative to improve the utilization rate of educational information resources and improve the management level and education quality of schools by intelligent management, teaching, scientific research, and life mode. This paper proposes an integrated processing method of educational information resources based on edge computing, constructs a sparse dictionary based on “cloud-end,” and compresses the data, which effectively improves the transmission and interaction efficiency of educational information resources. However, in the multinode edge calculation, the update cycle of the global dictionary is different, and the introduction of asynchronous federated learning mechanism can effectively solve the problem of time asynchrony. In asynchronous federated learning, the nodes have problems such as uneven learning samples and different learning progress. It is obviously unreasonable for these more different nodes to update the global parameters in the parameter server equally. In this paper, we design a two-weight update method to correct the global dictionary update, which solves the problem of unbalanced dictionary update and effectively solves the problem of integrating educational information resources. There are also some deficiencies in the work, especially in the absence of further optimization of the node layout and the dictionary update cycle, which will be the focus of the follow-up work.



---

*Source: 9348413-2022-05-20.xml*