Federated learning is a set-up in which multiple clients collaborate to solve machine learning problems, which is under the coordination of a central aggregator. This setting also allows the training data decentralized to ensure the data privacy of each device. Federated learning adheres to two major ideas: local computing and model transmission, which reduces some systematic privacy risks and costs brought by traditional centralized machine learning methods. The original data of the client is stored locally and cannot be exchanged or migrated. With the application of federated learning, each device uses local data for local training, then uploads the model to the server for aggregation, and finally the server sends the model update to the participants to achieve the learning goal. To provide a comprehensive survey and facilitate the potential research of this area, we systematically introduce the existing works of federated learning from five aspects: data partitioning, privacy mechanism, machine learning model, communication architecture and systems heterogeneity. Then, we sort out the current challenges and future research directions of federated learning. Finally, we summarize the characteristics of existing federated learning, and analyze the current practical application of federated learning.

A survey on federated learning

Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it to protect the privacy of users whereas anonymous data remains useful for legitimate information consumers. Many anonymization models, algorithms, frameworks, and prototypes have been proposed/developed for privacy preserving data publishing (PPDP). These models/algorithms anonymize users’ data which is mainly in the form of tables or graphs depending upon the data owners. It is of paramount importance to provide good perspectives of the whole information privacy area involving both tabular and SN data, and recent anonymization researches. In this paper, we presents a comprehensive survey about SN (i.e., graphs) and relational (i.e., tabular) data anonymization techniques used in the PPDP. We systematically categorize the existing anonymization techniques into relational and structural anonymization, and present an up to date thorough review on existing anonymization techniques and metrics used for their evaluation. Our aim is to provide deeper insights about the PPDP problem involving both graphs and tabular data, possible attacks that can be launched on the sanitized published data, different actors involved in the anonymization scenario, and major differences in amount of private information contained in graphs and relational data, respectively. We present various representative anonymization methods that have been proposed to solve privacy problems in application-specific scenarios of the SNs. Furthermore, we highlight the user’s re-identification methods used by malevolent adversaries to re-identify people uniquely from the privacy preserved published data. Additionally, we discuss the challenges of anonymizing both graphs and tabular data, and elaborate promising research directions. To the best of our knowledge, this is the first work to systematically cover recent PPDP techniques involving both SN and relational data, and it provides a solid foundation for future studies in the PPDP field.

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

With the rapid development of industrial informatization and deep learning technology, modern data-driven fault diagnosis (MIFD) methods based on deep learning have been receiving attention from the industry. However, most of these methods require sufficient training samples to achieve the desired diagnostic effect, and the scarcity of fault samples in the actual industrial environment leads to the limitation of the development of MIFD methods. In addition, data-driven fault diagnosis methods often need to face cross-load or even cross-domain problems across different devices due to changes in equipment operating conditions and production requirements. In this paper, we design a parameter optimization and feature metric-based fault diagnosis method with few samples, called model unknown matching network model, for the problem of sparse fault samples and cross-domain between data sets in real industrial environments. The method combines both a parametric optimization-based meta-learning network, which extracts optimization information to adapt between different domains, and a metric-based metric learning network, which extracts metric information for similarity discriminations. The experimental results show that the method outperforms the current baseline method for the five-shot fault diagnosis problem of bearings under limited data conditions and achieves an accuracy of up to 94.4 % in cross-device diagnosis experiments from bearings to gas regulators, indicating the feasibility of the method. The features are visualized by T-SNE to show the validity of the model.

Few shot cross equipment fault diagnosis method based on parameter optimization and feature mertic

The ongoing data explosion introduced unprecedented challenges to the information security of communication networks. As images are one of the most commonly used information transmission carriers; therefore, their data redundancy analysis and screening are of great significance. However, most of the current research focus on the algorithm improvement of commonly used image datasets. Thus, we should consider an important question: Is there data redundancy in the open datasets? Considering the factors of model structures and data distribution to ensure the generalization, we conducted extensive experiments to compare the average accuracy based on few random data to the baseline accuracy based on all data. The results show serious data redundancy in the open datasets from different domains. For instance, with the aid of deep model, only 20% data can achieve more than 90% of the baseline accuracy. Further, we proposed a novel entropy-based information screening method, which outperforms the random sampling under many experimental conditions. In particular, considering 20% of data, for the shallow model, the improvement is approximately 10%, and for the deep model, the ratio to the baseline accuracy increases to greater than 95%. Moreover, this work can also serve as a new way of learning from a few valuable samples, compressing the size of existing datasets and guiding the construction of high-quality datasets in the future. 

Entropy-based redundancy analysis and information screening

Chinese agricultural diseases and pests named entity recognition with multi-scale local context features and self-attention mechanism

Few-shot learning aims at training a model that can effectively recognize novel classes with extremely limited training examples Few-shot learning via meta-learning can improve the performance on novel tasks by leveraging previously acquired knowledge as a prior when the training examples are extremely limited However, most of these existing few-shot learning methods involve parameter transfer, which usually requires sharing models trained on the examples for specific tasks, thus posing a potential threat to the privacy of data owners To tackle this, we design a novel secure collaborative few-shot learning framework More specifically, we incorporate differential privacy into few-shot learning through adding the calibrated Gaussian noise to its optimization process to prevent sensitive information in the training set from being leaked To prevent potential privacy disclosure to other participants and the central server, homomorphic encryption is integrated while calculating global loss functions and interacting with a central server Furthermore, we implement our framework on the classical few-shot learning methods such as MAML and Reptile, and extensively evaluate its performance on Omniglot, Mini-ImageNet and Fewshot-CIFAR100 datasets The experimental results demonstrate the effectiveness of our framework in both utility and privacy

Secure collaborative few-shot learning

In the era of big data, data has gradually become an important productivity driving social progress. Accelerating the development and sharing of data resources is an inherent requirement of government transformation. Named entity recognition has a wide range of applications in the fields of information extraction and information retrieval, so its research is of great significance. Due to the complexity of Chinese structure and the lack of mature domestic corpora, the research on Chinese named entity recognition faces enormous challenges. Based on the analysis of the actual characteristics of named entities in Chinese text, this paper proposes a Chinese named entity recognition method based on rules and conditional random fields. The rule-based method is used to identify the named entities of digital expressions and time expressions. Named entity recognition of names of people, places, and organizations by combining rules and conditional random fields. Through the experiment, the results are analyzed, the best template is adjusted, and a complete and accurate Chinese named entity recognition method is designed and implemented. The experimental results show that the new method can effectively identify the named entities, improve the processing speed and efficiency, and has certain practical value.

Chinese Named Entity Recognition Based on Rules and Conditional Random Field

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Secure collaborative few-shot learning

Chinese Named Entity Recognition Based on Rules and Conditional Random Field