Predictive Analysis and Simulation of College Sports Performance Fused with Adaptive Federated Deep Learning Algorithm

Abstract

With the widespread use of intelligent teaching, data containing student performance information continues to emerge, and artificial intelligence technology based on big data has made a qualitative leap. At present, the prediction of college students’ sports performance is only based on the past performance, and it does not reflect the student’s training effect very well. In order to solve these problems, this paper puts forward the analysis and simulation of college sports performance fusion with adaptive federated deep learning algorithm, aiming to study the influencing factors of student sports performance and suggestions for improvement. This paper uses an adaptive federated learning method and a personalized federated learning algorithm based on deep learning and then proposes a student performance prediction method. These methods integrate the quantitative methods of motor skill assessment and establish standards for college students, which are good standards for evaluating college students’ sports skills. This paper adopts the performance prediction framework and then establishes the sports performance prediction model. Through the analysis of sports performance analysis examples, it is concluded that the model proposed in this paper can accurately predict the student’s sports performance, and the average accuracy rate of each sports item has reached 91.7%.

Landscape Design of Rural Characteristic Towns Based on Big Data Technology

“…e proposed method is more efficient methods than deep learning algorithms. When the cloud point data registration results of the deep learning algorithm method [26][27][28][29] and the proposed method are obtained, and the cloud point data are continuously improved, the convergence of the proposed method's cloud point data registration results is lower than that of the deep learning algorithm method. It is proved that the convergence of the proposed method is better than that of the deep learning algorithm method.…”

[Retracted] System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm

“…Sun [ 24 ] proposed an adaptive federated learning technique and a personalized federated learning algorithm based on DL to investigate the elements impacting pupil sports performance and make recommendations for development. It was resolved that the model provided in this study can precisely forecast the pupil's athletic performance with the average accurateness ratio.…”

“…Sun [24] proposed an adaptive federated learning technique and a personalized federated learning algorithm based 2…”

ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression

“…(Raginsky et al, 2017;Wibisono, 2019;Salim et al, 2019;Karagulyan and Dalalyan, 2020)). In particular, a line of research has been initiated on federated sampling Langevin algorithms, which combine LMC with existing optimization mechanisms: LMC+FedAvg (McMahan et al, 2017;Deng et al, 2021;, LMC+MARINA Sun et al, 2022), LMC+QSGD (Alistarh et al, 2017;Vono et al, 2022). Our work continues the logic of these papers by adding the error-feedback mechanisms EF21 and EF21-P to the classic LMC algorithm in the federated setting.…”

“…Vempala and Wibisono (2019) proved the convergence of the LMC under Log-Sobolev inequality. Later, Sun et al (2022) used this as a general scheme for LMC with stochastic gradient estimators in the context of federated Langevin sampling. We simplify their proof and adapt it to our setting.…”