Xinyu Zhang

Federated learning (FL) offers a distributed approach for the collaborative training of machine learning models across decentralized clients while safeguarding data privacy. This characteristic makes FL well suited for privacy-sensitive fields such as healthcare and finance. However, addressing the heterogeneity caused by nonindependent and identically distributed (non-IID) data remains a significant challenge for traditional FL methods. To address these issues, the enhancing clustered federated learning with adaptive similarity (AS-CFL) algorithm, which dynamically forms client clusters based on model update similarity and uses a forward-incentive mechanism to improve collaborative training efficiency among similar clients, is proposed in this study. Experimental results on the MNIST and EMNIST datasets reveal that compared with baseline methods such as the CFL, IFCA, and FedAvg models, the AS-CFL algorithm achieves faster convergence—reducing the number of communication rounds by approximately 20%—while maintaining competitive accuracy, demonstrating its effectiveness in heterogeneous FL scenarios.

In recent years, multi-view data often suffer from incompleteness owing to environmental factors, equipment failures. Thus Incomplete Multi-View Clustering (IMVC) has become an important research focus, which aims to alleviate the adverse impacts of missing views and leverage inter-view complementary information to enhance clustering performance. However, existing IMVC methodologies suffer from three critical limitations: 1) Inadequate integration of cross-view learning and cross-instance learning; 2) Lack of explicit modeling for dynamic interactions between view-specific information and cross-view shared semantics; 3) Inability to dynamically capture high-order topological correlations under view-missing conditions, leading to semantic misalignment among samples. To address these challenges, we propose an IMVC framework CrossNet-VGA based on variational collaboration and graph attention fusion. Specifically, We formulate a novel multi-view evidence lower bound to explicitly separate view-specific latent variables and cross-view shared latent variables, and achieve inter-view semantic fusion by integrating variational distributions shared across views. Contrastive learning is employed to maximize mutual information and promote feature distribution uniformity, thereby achieving consistent representation learning. We employ dynamic k-nearest neighbor graph construction and multi-head graph attention mechanisms to capture the inter-sample deep topological correlations, achieving robust structural alignment. Comprehensive experiments conducted on 6 public datasets demonstrate that CrossNet-VGA significantly outperforms the competing methods both on accuracy and robustness. The anonymous code of this work is available on GitHub at https://github.com/ggg2111/2025-TIP-CrossNet-VGA.

In recent years, incomplete multi-view clustering (IMVC) has attracted considerable attention for its ability to acheieve effective clustering results through the integration of key information amidst missing view. However, the existing IMVC methods are still faced with 3 limitations: (1) They exhibit deficiencies in considering the weight distribution within views, (2) they ignore the varying contributions of different views to the common consistent representation, and (3) they struggle to sufficiently extract and recover the vital information within incomplete views. To address these limitations, we incorporates local reasoning and correlation analysis to design an incomplete multi-view clustering method(IMVCLRCA), which introduces a new strategy of feature learning and missing view recovery, fully exploiting local similarity and structural continuity within views and performing precise local reasoning recovery on missing data. By maximizing mutual information between views through contrastive learning, we achieve the consistent representation learning of multiple views. Furthermore, based on semantic consistency, we comprehensively consider the correlation between views, utilized a weight matrix to fuse cross-view data, and constructed a view with a correlation structure, ultimately obtaining a common consistent representation. We conduct extensive experiments on 4 public datasets including Caltech101-20, BBCSport, Scene-15, and LandUse-21. Experimental results demonstrate that IMVCLRCA has higher accuracy and robustness compared to the state-of-the-art IMVC methods. The anonymous code of this project is available on GitHub at https://github.com/ggg2111/2025WSDM-IMVCLRCA.

Summary: This paper proposes a novel Dual-level Joint Adaptation and Anti-forgetting (DJAA) framework for unsupervised domain adaptive person re-identification (ReID). It addresses critical challenges in lifelong person ReID, including catastrophic...

The challenge of optimally placing facilities to maximize coverage within road networks is a critical problem with significant implications for urban planning, emergency response, and the development of sustainable infrastructure. For instance, strategically locating fire stations or electric vehicle (EV) charging stations along a road network can greatly enhance public safety and support the adoption of clean transportation technologies. However, determining these optimal placements is computationally challenging, particularly when accounting for factors like road network distances and coverage radius. Traditional methods, such as greedy algorithms, offer a reasonable approximation but are limited by high computational complexity, making them less suitable for large-scale transportation networks. In response, our research introduces two novel algorithms designed to improve both the efficiency and scalability of the $k$ -radius coverage problem. The first algorithm achieves a strong approximation with significantly reduced time complexity, while the second employs a sketch-based approach, offering a nearly linear time complexity relative to the number of edges. Although the second algorithm sacrifices some approximation accuracy, it offers substantial gains in computational speed, making it particularly valuable for large-scale transportation networks. Extensive experiments on large-scale real-world road networks demonstrate the superior performance of our proposed methods compared to existing solutions.

Various mobile applications that comprise dependent tasks are gaining widespread popularity and are increasingly complex. These applications often have low-latency requirements, resulting in a significant surge in demand for computing resources. With the emergence of mobile edge computing (MEC), it becomes the most significant issue to offload the application tasks onto small-scale devices deployed at the edge of the mobile network for obtaining a high-quality user experience. However, since the environment of MEC is dynamic, most existing works focusing on task graph offloading, which rely heavily on expert knowledge or accurate analytical models, fail to fully adapt to such environmental changes, resulting in the reduction of user experience. This paper investigates the task graph offloading in MEC, considering the time-varying computation capabilities of edge computing devices. To adapt to environmental changes, we model the task graph scheduling for computation offloading as a Markov Decision Process (MDP). Then, we design a deep reinforcement learning algorithm (SATA-DRL) to learn the task scheduling strategy from the interaction with the environment, to improve user experience. Extensive simulations validate that SATA-DRL is superior to existing strategies in terms of reducing average makespan and deadline violation.