In federated news recommendation, representing each news with a single embedding entangles shared semantics with user-specific preference shifts, which weakens personalization under non-IID users and strains communication when catalogs are large. To address this problem, we introduce FedDEN, a Dual News-Embedding framework that cleanly disentangles the two roles of item representation: a server-maintained global embedding captures cross-user semantics, while a lightweight client residual captures user-dependent deviations only where needed. Based on this core and tailored to the peculiarities of news recommendation, we attach two targeted mechanisms: (i) a minimal cold-start bias that substitutes residuals when histories are scarce, enabling immediate participation without extra rounds; and (ii) an alignment-and-sparsity regularization that keeps residuals complementary to the global table while promoting a compact, communicable global news embedding via proximal updates. Experiments on three real world datasets show consistent gains over strong centralized and federated baselines on metrics, together with robust behavior under cold-start settings, and favorable per-round communication. By solving the conflation of shared semantics and user-specific shifts at its source, FedDEN achieves strong personalization with practical efficiency. Specifically, it outperforms centralized baseline NRMS by 13.61 % and FedRec by 15.59 %.

Federated Learning enables multiple clients to collaboratively train a model without sharing their individual data, thereby protecting local data privacy. However, attackers, such as untrusted servers, can still compromise the privacy of clients’ local training data through various inference attacks. One feasible approach to protect client privacy during training is the incorporation of differential privacy. Nevertheless, achieving an ideal level of privacy protection with differential privacy often degrades the model’s performance, significantly reducing its accuracy. To enhance model accuracy while minimizing the additional client heterogeneity introduced by differential privacy, this paper proposes a method that integrates personalized layers and data transformations, FusTP-FL. The core of our FusTP-FL is the incorporation of personalized layers and personalized data transformations within the client’s local training model, which further reduces client heterogeneity and improves model accuracy. We evaluated the model’s accuracy on six common datasets; experimental results demonstrate that the proposed FusTP-FL effectively enhances model accuracy across two different differential privacy modes (CDP and LDP), increasing it by up to 45%. Furthermore, we show that compared to PRIVATEFL, our method achieves lower client heterogeneity.

The swift advancement of distributed computing has enhanced the support for big data and massive-scale models. Yet, delivering superior services to manage large and intricate network flows in data center networks remains a formidable challenge. In this paper, we present ReQ-tank, an intricate flow scheduling approach based on a multi-level feedback queue (MLFQ) devised to achieve flow prioritization and efficient flow scheduling. ReQ-tank employs a two-tier scheduling strategy: On the flow scheduling layer, priority queues are segmented into two categories, and the flows within high-priority queues follow a strict priority scheduling, while those in low-priority queues adhere to differential weighted Round-robin scheduling; On the packet scheduling layer, ReQ-tank modifies the priority of initially high-priority re-transmitted packets to facilitate fine-grained data packet scheduling. We carry out simulation experiments on web search workloads and data mining workloads. Experimental results demonstrate that ReQ-tank can curtail packet wait time in the network, significantly truncate the flow completion time (FCT) of delay-sensitive flows and counteract the issue of flow starvation in traditional strict priority queues. Consequently, ReQ-tank is deemed more suitable for complex distributed network applications.

Data classification of users’ electricity consumption provides an in-depth analysis for users’ electricity consumption status, which plays a vital role in the management and distribution of electric energy. So, some data classification methods have been proposed to solve the classification problem of electricity consumption data. However, plaintext-based data classification may bring about the privacy leakage of electricity consumption data. In this paper, we propose a privacy-preserving classification scheme for electricity consumption data under fog computing-based smart metering system, which is based on convolutional neural network (CNN) model with fully homomorphic method (CKKS). The target of our proposed scheme is to solve the leakage problem of private electricity consumption data during the classification procedure. In our scheme, an improved K-means-based labeling algorithm is constructed to process historical electricity consumption data, which is used as the sample data to train the CNN classification model by cloud server. Also, the fog nodes are only permitted to obtain the related ciphertext parameters of the trained CNN model, and perform the classification of ciphertext-based electricity consumption data generated by fully homomorphic method. Based on the classical testing data, the experimental results show that our proposed classification scheme can provide the high classification accuracy of electricity data while protecting the privacy of electricity data.

Land cover classification and change detection plays an important role and significance in geographical conditions and environmental monitoring, which is a hot and difficult remote sensing topic. The difficulties in land cover remote sensing are how to realize the detecting by a quantitative and automatic way. At present, there is no general classification algorithm which is proper for different multi-remote sensing data's quantitative processing. In this paper, a multi-source remote sensing image classification method based on spectral reflectance characteristics is studied. Using the geometric and radiometric calibration parameters, sensor spectral response function and satellite-earth-sun's orbit parameters, multi remote sensing data's ground reflectance and land cover monitoring classification could be realized automatically. In earthquake evaluation, two GF2 multispectral remote sensing images are processed. The typical land cover elements, including vegetation, water and bare land, are detected and classified. The evaluation of land cover change proportion is calculated quantitatively and automatically.

With the development of mobile devices and GPS, plenty of Location-based Services (LBSs) have emerged in these years. LBSs can be applied in a variety of contexts, such as health, entertainment, and personal life. The location based data that contains significant personal information is released for analysing and mining. The privacy information of users can be attacked from the published data. In this paper, we investigate the problem of privacy-preservation of density distribution on mobility data. Different from adding noises into the original data for privacy protection, we devise the Generative Adversarial Networks (GANs) to train the generator and discriminator for generating the privacy-preserved data. We conduct extensive experiments on two real world mobile datasets. It is demonstrated that our method outperforms the differential privacy approach in both data utility and attack error.

This paper presents a plug-in based integrated development framework for industrial control system named P-IDP4ICS which adopts the role-based access control technique, named RBAC. And the implementation mechanisms of P-IDP4ICS are explained in this paper. The P-IDP4ICS framework consists of some components, such as Extension Protocol, Kernel, Plug-in Registry and Host Application. Among those components, Extension Protocol component can keep the industrial control software customizable and scalable, and the reusing plug-ins which have been well developed like Host Application plug-in can improve reusing capability of industrial control software. The plug-ins in P-IDP4ICS are presented in the form of DLLs, which protect the source code. Furthermore, P-IDP4ICS which implements the RBAC model reduces the complexity of authorization management and the costs of system maintenance. Finally, this paper reconstructs a Sinter Integrated Control Expert System through P-IDP4ICS framework and has proved its effectiveness.

This paper presents a land-use change forecasting model in development zones based on RS and GIS. We argue that, in order to explore a feasible method for forecasting land-use change in development zones, GIS (Geography Information System) and RS (Remote Sensing) are used to analyze the different kinds of land-use change process and situation dynamically and quantitatively. Land-use change is represented by transition probability that is calculated from Quick8ird, IKNOS and SPOT imagery. And based on that a Markov transition matrix of different land-use categories is built up to forecast the land-use change trend. According to this method, a utility system is developed using C# and ArcEngine as tools. The study shows this system is credible and practical, and improves the efficiency of land management in the development zones as a useful land-use monitoring tool.