The Dilemma and Path Innovation of Education in Ethnic Minority Areas

Due to historical culture, natural geography, economic foundation and other reasons, the development of ethnic education still faces some special difficulties and outstanding problems, and the overall development level is relatively low. Education in some ethnic minority areas faces the real predicament of imbalanced educational resources, great shackles in traditional ideas, and difficulty in promoting education informatization. In order to accelerate the development of education in ethnic minority areas and achieve long-term peace and stability in the country, it is necessary to establish universal and special policies and promote long-term education mechanisms, and innovate the education path in ethnic minority areas in order to solve these dilemmas

STSyn: Speeding Up Local SGD with Straggler-Tolerant Synchronization

Synchronous local stochastic gradient descent (local SGD) suffers from some workers being idle and random delays due to slow and straggling workers, as it waits for the workers to complete the same amount of local updates. In this paper, to mitigate stragglers and improve communication efficiency, a novel local SGD strategy, named STSyn, is developed. The key point is to wait for the $K$ fastest workers, while keeping all the workers computing continually at each synchronization round, and making full use of any effective (completed) local update of each worker regardless of stragglers. An analysis of the average wall-clock time, average number of local updates and average number of uploading workers per round is provided to gauge the performance of STSyn. The convergence of STSyn is also rigorously established even when the objective function is nonconvex. Experimental results show the superiority of the proposed STSyn against state-of-the-art schemes through utilization of the straggler-tolerant technique and additional effective local updates at each worker, and the influence of system parameters is studied. By waiting for faster workers and allowing heterogeneous synchronization with different numbers of local updates across workers, STSyn provides substantial improvements both in time and communication efficiency.Comment: 12 pages, 10 figures, submitted for transaction publicatio

ABS: Adaptive Bounded Staleness Converges Faster and Communicates Less

Wall-clock convergence time and communication rounds are critical performance metrics in distributed learning with parameter-server setting. While synchronous methods converge fast but are not robust to stragglers; and asynchronous ones can reduce the wall-clock time per round but suffers from degraded convergence rate due to the staleness of gradients, it is natural to combine the two methods to achieve a balance. In this work, we develop a novel asynchronous strategy that leverages the advantages of both synchronous methods and asynchronous ones, named adaptive bounded staleness (ABS). The key enablers of ABS are two-fold. First, the number of workers that the PS waits for per round for gradient aggregation is adaptively selected to strike a straggling-staleness balance. Second, the workers with relatively high staleness are required to start a new round of computation to alleviate the negative effect of staleness. Simulation results are provided to demonstrate the superiority of ABS over state-of-the-art schemes in terms of wall-clock time and communication rounds

The Association of Parent-Child Communication With Internet Addiction in Left-Behind Children in China: A Cross-Sectional Study

Objective: Internet addiction has emerged as a growing concern worldwide. This study aimed to compare the prevalence of Internet addiction between left-behind children (LBC) and non-left-behind children (non-LBC), and explore the role of paternal and maternal parent-child communication on LBC. Methods: We conducted a cross-sectional survey in rural areas in Anhui, China. The complete data were available from 699 LBC and 740 non-LBC. Multivariable logistic regression was used to examine 1) whether LBC were more likely to develop Internet addiction, and 2) the association between parent-child communication and Internet addiction among LBC. Results: LBC had a higher likelihood to report Internet addiction when compared to non-LBC (OR = 2.03, 95%CI = 1.43–2.88, p \u3c 0.001). Among LBC, parent-child communication (both mother-child and father-child) was protective factor for children’s Internet addiction. The role of mother-child communication played well among male LBC. Conclusions: The lack of parental supervision may lead to Internet addiction. It is highly recommended for migrant parents to improve the quality of communication with their children. Also, gender-matching effects should be considered in the relationship between children’s behavior and parental factors

DRAG: Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data

Local stochastic gradient descent (SGD) is a fundamental approach in achieving communication efficiency in Federated Learning (FL) by allowing individual workers to perform local updates. However, the presence of heterogeneous data distributions across working nodes causes each worker to update its local model towards a local optimum, leading to the phenomenon known as ``client-drift" and resulting in slowed convergence. To address this issue, previous works have explored methods that either introduce communication overhead or suffer from unsteady performance. In this work, we introduce a novel metric called ``degree of divergence," quantifying the angle between the local gradient and the global reference direction. Leveraging this metric, we propose the divergence-based adaptive aggregation (DRAG) algorithm, which dynamically ``drags" the received local updates toward the reference direction in each round without requiring extra communication overhead. Furthermore, we establish a rigorous convergence analysis for DRAG, proving its ability to achieve a sublinear convergence rate. Compelling experimental results are presented to illustrate DRAG's superior performance compared to state-of-the-art algorithms in effectively managing the client-drift phenomenon. Additionally, DRAG exhibits remarkable resilience against certain Byzantine attacks. By securely sharing a small sample of the client's data with the FL server, DRAG effectively counters these attacks, as demonstrated through comprehensive experiments