High-dimensional tensors are a cornerstone of context-aware recommendation systems. However, deploying tensor-based models within a federated learning framework faces formidable hurdles in computational efficiency, privacy preservation, and system robustness. These challenges are exacerbated by the untrusted server, which could infer sensitive user data or corrupt the global model. To systematically address these obstacles, we design and implement CSV-FTL, a Cloud-Edge Synergistic Secure and Verifiable Federated Tensor Learning framework. Our framework’s core innovations are fourfold: (1) a natively designed federated tensor model optimized for high efficiency in distributed environments; (2) a lossless double-masking mechanism that achieves strong privacy without compromising model accuracy; (3) a reconstruction algorithm for user dropout tolerance; and (4) a homomorphic hashing scheme enabling users to verify the integrity of server-side aggregation. Consequently, CSV-FTL strikes a superior balance between recommendation accuracy, computational cost, and security guarantees. To the best of our knowledge, this is the first work to seamlessly integrate a native federated tensor model with lossless privacy, dropout tolerance, and verifiable aggregation within a single, unified framework. Both rigorous theoretical analysis and extensive experiments on four real-world recommendation-related datasets validate the provable security and superior performance of our framework over state-of-the-art methods.

Sparse mobile crowdsensing (SMCS) is a cost-effective paradigm aimed at recruiting workers to complete sensing tasks and inferring the remaining unobserved data, with broad applications in large-scale, fine-grained monitoring services. In SMCS, spatial coverage of the sensing area or global completion accuracy is typically used as the performance metric. However, in many real-world service scenarios (e.g., temperature, humidity, air quality monitoring), users are generally only interested in data from their specific regions and expect the highest possible data accuracy. In such cases, relying solely on coverage or global completion error fails to adequately assess the quality of the platform’s service. To address this and satisfy users’ sensing demands as much as possible while maintaining low sensing costs, we propose the Demand-Driven Framework with Neighborhood-Aware Data Reconstruction (D2-SMCS), which integrates regional population demand calculation, dynamic clustering, and data reconstruction. Unlike existing approaches, we introduce quality of service (QoS) as a performance metric based on regional population demand. First, we quantify the interest level of sensing tasks in different regions by considering factors such as population demand and data fluctuation. Based on this quantification, the dynamic clustering module selects the regions most beneficial for accurate data completion. Finally, to overcome the limitation of traditional matrix completion methods in capturing short-term variations, we propose an innovative Neighborhood-Aware Latent Matrix Completion (NALMC) approach to infer and complete the unobserved regions. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework.

The heterogeneity of local data and client performance, along with real-world system risks, is driving the evolution of federated learning (FL) towards personalized, model-heterogeneous, and decentralized approaches. However, due to the differing structures of heterogeneous models, it is hard to use them to identify clients with similar data distributions and further enhance the personalization of local models. Therefore, how to deal with data heterogeneity to obtain superior personalized local models for clients, while simultaneously addressing model heterogeneity and system risks is a challenging problem. In this paper, we propose a novel personalized FL framework with fine-gRained layEr aggregAtion andDecentralized cluStering (${\sf Reads}$), which integrates four key components: (1) deep mutual learning with privacy guarantee for model training and privacy preservation, (2) fine-grained layer similarity computation among heterogeneous model layers, (3) fully decentralized clustering for soft clustering of clients based on layer similarities, and (4) personalized layer aggregation for capturing common knowledge from other clients. Through${\sf Reads}$, clients obtain personalized models that accommodate model heterogeneity, while the system ensures robustness against a single point of failure. Extensive experiments demonstrate the efficacy of${\sf Reads}$in achieving these goals.

The pervasive distribution of data across clients with privacy concerns and heterogeneous performance in edge networks presents a significant opportunity to enhance AI model performance. Federated learning (FL) enables a model owner (MO) to recruit these clients, offering compensation for their contributions, and to improve model quality by aggregating knowledge from their locally trained models. However, several challenges arise in this process. Clients may decline participation if they do not achieve positive utility. Moreover, due to constraints in memory, computing, and communication resources, some clients can only train lightweight models that represent partial versions of the global model. Importantly, the MO's pricing for client contributions and the proportions of local model training are interdependent, collectively influencing client utilities and participation decisions. To address these challenges, we first model the utility functions of both the MO and the clients, accommodating the support for lightweight local models. We then formulate their interactions as a Stackelberg game and theoretically prove the existence of a Nash equilibrium. Based on this equilibrium, we derive optimal collaboration strategies for both the MO and the clients. Additionally, we design an efficient approximation algorithm to enable the MO to maximize its utility by selecting suitable clients to participate in FL. Finally, extensive experiments validate our theoretical findings, demonstrating the superior performance and effectiveness of the proposed algorithms

Human-robot object handovers are essential for robots to effectively serve human needs in various domains of human–robot interaction and collaboration, yet remain a significant challenge. Remarkable progress has been made by parallel-jaw gripper robots in grasp generation and motion planning for handovers, while few studies address this issue using anthropomorphic hands, necessitating the ability to handle higher collision probabilities and lower approaching space under occluded situations. In this article, we present a reactive human-to-robot dexterous handover framework for anthropomorphic hands. The closed-loop framework employs an effective collision detection and grasp selection approach to ensure safe and smooth motion in unstructured environments. We implement a handover system using a UR5 robot arm and a Schunk SVH Hand based on the presented framework, which can react to human motion during the handover process, generalize to diverse objects with different 6-DoF poses, and execute suitable grasp configurations. The generalizability, reliability, and efficiency of our method are demonstrated through the handover of 30 novel objects, a system ablation study for submodule evaluation, and a user study assessment involving eight participants.

Deep learning is increasingly utilized in data-driven tasks in Vehicular Ad-hoc Networks (VANETs) such as traffic sign recognition or pedestrian detection, and is expected to fulfill autonomous driving. These applications involve large amounts of discrete private data. Federated learning can effectively address the issues of data islands and security. However, it is challenging to apply centralized framework to meet the rigorous requirements of intelligent services in VANETs, such as ultra-low latency, high security and reliability. In this work, we propose a privacy-preserving deep learning scheme named FEDL, which is built on a decentralized framework (abbreviated as EDVAN). This customized EDVAN structure decomposes computing tasks into each edge computing node equally. By combining the Voronoi diagram the network communication delay and overhead are alleviated tremendously. Furthermore, the FEDL scheme utilizes functional encryption to establish secure data communication and drop-allowance parameter aggregation module, achieving to resist inference attacks and eliminate the straggler effect. Finally, we apply the NS-3 simulator to simulate EDVAN’s communication environment. Three real traffic datasets are utilized to show the FEDL model is accurate lossless and efficient. Our code is available athttps://github.com/HuangZengyi/FEDL.

Online medical prediagnosis systems have already shown great achievement in providing the guidance of healthcare services with lower time and cost. Achieving a high-precision medical primary diagnosis system faces many severe challenges on the privacy of individual health information, the distributed storage of medical data and the diversity of the disease. In this paper, we propose an efficient and privacy-preserving framework for obtaining a pre-clinical guide model, which allows an authorized data analysis center to train a disease classifier using a combination of medical data gathered from different entities. Our proposed scheme is based on soft-margin support vector machine (SVM) which takes Taylor polynomial of exponential-loss as penalty. Our scheme achieves the following advantages: the trained model can tolerate some abnormal samples therefore has higher generalization ability, and the training process can constraint the inefficient operations in the encrypted domain thus leads to the availability of partial homomorphic encryption system. Lately, we prove that the proposed scheme achieves the goal of medical prediagnosis system construction and data without privacy leakage to data analysis center and model parameters without exposure to data providers, as well as demonstrating its utility and efficiency using real-world medical datasets.

Two-path cancellation schemes have attracted much attention due to its effectiveness in suppressing intercarrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) systems. In this paper, we derive the exact closed-form expressions of the signal-to-interference-plus-noise ratio for two-path cancellation schemes in multipath fading channels as well as their upper bounds, which are much simpler to formulate yet tight enough to provide useful insight. By properly approximating the residual ICI as Gaussian process, we further derive the closed-form expressions of bit error rate (BER) for an uncoded OFDM system with QPSK modulation in multipath fading channels. More importantly, the derived BER expressions take into account the important phase rotation effect. Monte-Carlo simulations are conducted to validate our analyses and derivations.

Imaginary stroke technique has been proved to be an effective solution to the problem of the stroke connection in online handwritten character recognition. However, it may cause confusions among characters with similar but actually different trajectories after adding imaginary strokes. In this paper, we first investigate both the benefit and the defect of the imaginary stroke technique, and then two modified methods are proposed under the framework of feature fusion and local feature enhance respectively. With the proposed methods, the feature of imaginary strokes is employed to unify the writing styles and the feature of real strokes is enhanced to strengthen discriminability. Experimental results for handwritten Chinese character recognition indicate that comparing with feature without imaginary strokes and feature with imaginary strokes, our proposed methods provide about 3%~8% and 1%~4% recognition accuracy improvement respectively.