Adaptive Local Steps Federated Learning with Differential Privacy Driven by Convergence Analysis

Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations without sharing data. However, while FL ensures that the raw data is not directly accessible to external adversaries, adversaries can still obtain some statistical information about the data through differential attacks. Differential Privacy (DP) has been proposed, which adds noise to the model or gradients to prevent adversaries from inferring private information from the transmitted parameters. We reconsider the framework of differential privacy federated learning in resource-constrained scenarios (privacy budget and communication resources). We analyze the convergence of federated learning with differential privacy (DPFL) on resource-constrained scenarios and propose an Adaptive Local Steps Differential Privacy Federated Learning (ALS-DPFL) algorithm. We experiment our algorithm on the FashionMNIST and Cifar-10 datasets and achieve quite good performance relative to previous work

A bearing fault detection method with low-dimensional compressed measurements of vibration signal

The traditional bearing fault detection method is achieved often by sampling the bearing vibration data under the Shannon sampling theorem. Then the information of the bearing state can be extracted from the vibration data, which is used in fault detection. A long-term and continuous monitoring needs to sample and store large amounts of raw vibration signals, which will burden the data storage and transmission greatly. For this problem, a new bearing fault detection method based on compressed sensing is presented, which just needs to sample and store a small amount of compressed observation data and uses these data directly to achieve the fault detection. Firstly, an over-complete dictionary is trained, on which the vibration signals corresponded to normal state can be decomposed sparsely. Then, the bearing fault detection can be achieved based on the difference of the sparse representation errors between the compressed signals in normal state and fault state on this dictionary. The fault detection results of the proposed method with different parameters are analyzed. The effectiveness of the method is validated by the experimental tests

The Integration of Ideological and Political Education into College Career Planning and Employment Guidance Courses: Design and Practice—A Case Study of the “Enjoyable Career” to “Professional Ambition”: Career Interests Exploring Course

In the contemporary intricate and dynamic job landscape, college career planning and employment advisory courses encounter novel problems. This study examines the effective integration of ideological and political education into career interest exploration courses to facilitate students’ transition from an “enjoyable career” to a “professional ambition”. This research integrates Holland’s career interest theory with ideological and political education to provide a multifaceted teaching strategy that highlights the connection between professional interests and social responsibility. The objective is to assist students in elucidating their professional interests while promoting appropriate career ideals and a feeling of social responsibility. Integrating ideological and political education promotes students’ career awareness and social responsibility while bolstering their flexibility and aptitude for sustained growth, eventually benefiting both people and society

Career adapt-ability scale -short form: Validation among early career stage of Chinese university graduates

The Career Adapt-Ability Scale (CAAS) is the favored method among researchers for measuring career adaptability. The 12-item version of CAAS-SF, which was made by Maggiori, Rossier, and Savickas based on a change to CAAS, has been slowly used by different groups in different countries and regions. As samples for the validation of the scale in this study, 571 Chinese university graduates in the early stages of their professions were chosen. Principal component analysis and confirmatory factor analysis suggest that CAAS-SF and CAAS have very similar psychological measurement features and factor structures. And the internal consistency of each subscale and total scale are equivalent to or greater than that of the CAAS assessment. These findings indicate that the CAAS-SF is a valid and reliable instrument for evaluating China’s career adaptability. In addition, limitations, issues for further research, and suggestions are emphasized

DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering

Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression. Recently, deep learning has facilitated the analysis of high-dimensional single-cell data. Unfortunately, deep learning models may leak sensitive information about users. As a result, Differential Privacy (DP) is increasingly used to protect privacy. However, existing DP methods usually perturb whole neural networks to achieve differential privacy, and hence result in great performance overheads. To address this challenge, in this paper, we take advantage of the uniqueness of the autoencoder that it outputs only the dimension-reduced vector in the middle of the network, and design a Differentially Private Deep Contrastive Autoencoder Network (DP-DCAN) by partial network perturbation for single-cell clustering. Since only partial network is added with noise, the performance improvement is obvious and twofold: one part of network is trained with less noise due to a bigger privacy budget, and the other part is trained without any noise. Experimental results of six datasets have verified that DP-DCAN is superior to the traditional DP scheme with whole network perturbation. Moreover, DP-DCAN demonstrates strong robustness to adversarial attacks

A bearing fault detection method with low-dimensional compressed measurements of vibration signal

The traditional bearing fault detection method is achieved often by sampling the bearing vibration data under the Shannon sampling theorem. Then the information of the bearing state can be extracted from the vibration data, which is used in fault detection. A long-term and continuous monitoring needs to sample and store large amounts of raw vibration signals, which will burden the data storage and transmission greatly. For this problem, a new bearing fault detection method based on compressed sensing is presented, which just needs to sample and store a small amount of compressed observation data and uses these data directly to achieve the fault detection. Firstly, an over-complete dictionary is trained, on which the vibration signals corresponded to normal state can be decomposed sparsely. Then, the bearing fault detection can be achieved based on the difference of the sparse representation errors between the compressed signals in normal state and fault state on this dictionary. The fault detection results of the proposed method with different parameters are analyzed. The effectiveness of the method is validated by the experimental tests

A bearing fault detection method with low-dimensional compressed measurements of vibration signal

The traditional bearing fault detection method is achieved often by sampling the bearing vibration data under the Shannon sampling theorem. Then the information of the bearing state can be extracted from the vibration data, which is used in fault detection. A long-term and continuous monitoring needs to sample and store large amounts of raw vibration signals, which will burden the data storage and transmission greatly. For this problem, a new bearing fault detection method based on compressed sensing is presented, which just needs to sample and store a small amount of compressed observation data and uses these data directly to achieve the fault detection. Firstly, an over-complete dictionary is trained, on which the vibration signals corresponded to normal state can be decomposed sparsely. Then, the bearing fault detection can be achieved based on the difference of the sparse representation errors between the compressed signals in normal state and fault state on this dictionary. The fault detection results of the proposed method with different parameters are analyzed. The effectiveness of the method is validated by the experimental tests

Differentially Private Federated Learning: A Systematic Review

In recent years, privacy and security concerns in machine learning have promoted trusted federated learning to the forefront of research. Differential privacy has emerged as the de facto standard for privacy protection in federated learning due to its rigorous mathematical foundation and provable guarantee. Despite extensive research on algorithms that incorporate differential privacy within federated learning, there remains an evident deficiency in systematic reviews that categorize and synthesize these studies. Our work presents a systematic overview of the differentially private federated learning. Existing taxonomies have not adequately considered objects and level of privacy protection provided by various differential privacy models in federated learning. To rectify this gap, we propose a new taxonomy of differentially private federated learning based on definition and guarantee of various differential privacy models and federated scenarios. Our classification allows for a clear delineation of the protected objects across various differential privacy models and their respective neighborhood levels within federated learning environments. Furthermore, we explore the applications of differential privacy in federated learning scenarios. Our work provide valuable insights into privacy-preserving federated learning and suggest practical directions for future research.Comment: 36page

A reference-grade wild soybean genome

Efficient crop improvement depends on the application of accurate genetic information contained in diverse germplasm resources. Here we report a reference-grade genome of wild soybean accession W05, with a final assembled genome size of 1013.2 Mb and a contig N50 of 3.3 Mb. The analytical power of the W05 genome is demonstrated by several examples. First, we identify an inversion at the locus determining seed coat color during domestication. Second, a translocation event between chromosomes 11 and 13 of some genotypes is shown to interfere with the assignment of QTLs. Third, we find a region containing copy number variations of the Kunitz trypsin inhibitor (KTI) genes. Such findings illustrate the power of this assembly in the analysis of large structural variations in soybean germplasm collections. The wild soybean genome assembly has wide applications in comparative genomic and evolutionary studies, as well as in crop breeding and improvement programs

A reference-grade wild soybean genome

Wild relatives of crop plants are invaluable germplasm for genetic improvement. Here, Xie et al. report a reference-grade wild soybean genome and show that it can be used to identify structural variation and refine quantitative trait loci