Federated recommendation systems (FRSs) have recently gained widespread attention due to their ability to train collaborative recommendation models without exchanging raw user data. However, existing FRSs face a severe challenge of data sparsity, which manifests at both the user and item levels. First, user data sparsity: some users may only have a small number of interactions with items, struggling to adequately train the personalized user embedding locally. Second, item data sparsity: some items may only receive a small number of user ratings, causing the global model to lack knowledge about them. Considering these, we propose the Knowledge Enhanced Federated Recommendation System named as KE-FedRS, of which the core idea is to enhance the knowledge of users with few interactions and items with few ratings at both the local and global levels. Specifically, at the local level, we introduce an auxiliary user embedding and average and aggregate this auxiliary embedding across similar users, thereby enriching the knowledge of the local user embedding. At the global level, we propose a hybrid client selection strategy based on item embedding discrepancies, prioritizing clients that exhibit greater divergence in item embeddings from others, thus enhancing the knowledge of items with fewer interactions in the global model. We conduct comprehensive experiments on four real-world datasets, and the results show that the proposed method consistently outperforms baseline approaches in terms of HR@10 and NDCG@10.

The rapid development of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) has led to the rise of Mobile Edge Computing (MEC), enabling low-latency task offloading in dynamic environments. However, existing offloading strategies struggle to generalize across diverse and evolving network topologies, often requiring retraining or fine-tuning when deployed in new scenarios. To address these challenges, we propose AtALT, a transferable task offloading framework that achieves zero-shot transferability. The acronym AtALT is derived from the key components of our method:Attention-AuxiliaryLearning-Transferable task offloading. AtALT integrates an attention-based encoder and an auxiliary learning module. The attention-based encoder dynamically computes topology-agnostic compatibility scores, allowing for flexible task offloading decisions across different network configurations. The auxiliary learning module predicts node states, regularizing the policy learning process and enhancing generalization. Experimental results demonstrate that AtALT outperforms existing methods in transferability and efficiency, making it suitable for deployment in previously unseen environments without the need for further training.

ABSTRACT Background Quantum federated learning presents a promising paradigm for privacy‐preserving collaborative training across distributed quantum devices. However, its scalability is hindered by the significant communication overhead associated with transmitting high‐dimensional, high‐precision quantum model parameters over classical networks. Methods To address this bottleneck, this paper proposes QFI‐Opt (Quantum Fisher Information‐guided Adaptive Optimization), a quantum adaptive communication optimization framework based on Quantum Fisher Information. QFI‐Opt establishes a “sensing‐compression‐regulation” pipeline that achieves communication efficiency while preserving quantum model fidelity. The framework uses QFI as a physically interpretable metric to dynamically assess quantum state sensitivity to parameter perturbations, enabling a progressive pruning strategy that removes low‐sensitivity parameters during training while retaining critical quantum features. Additionally, a dynamic bit‐width quantization mechanism adapts precision based on parameter importance, maximizing compression without compromising numerical stability. This is further complemented by a physics‐aware aggregation method that weighs client updates based on both local data volume and quantum information quality derived from QFI scores, improving global model robustness. Results Extensive evaluation on quantum convolutional neural networks demonstrates that QFI‐Opt significantly reduces per‐round communication overhead compared to baseline methods. Conclusions Simultaneously, the proposed framework maintains competitive model accuracy and convergence performance across diverse quantum architectures.

In the context of rapidly evolving electronic technology, numerous consumer electronic products have begun to employ recommendation systems to enhance user experience. Traditional recommendation systems utilize deep learning to predict user ratings for items; however, this approach requires users to share their data, leading to potential distrust in recommendations. The integration of federated learning into recommendation systems can achieve trustworthy recommendations, but current federated recommendation models necessitate multiple instances of user-item interaction data to learn global parameters. Therefore, this paper introduces Trustworthy Recommendation for Consumer Electronics Using Hypernetworks (TRCE) to ensure trustworthy recommendations for consumer electronics while also catering to users’ personalized needs. Initially, hypernetworks are used to rapidly initialize the recommendation model on the client side, with user preferences embedded as inputs to the hypernetwork to obtain personalized preferences; subsequently, within the client-side recommendation model, Item attribute content embeddings function as global information to offer more contextual facts; finally, attention residual blocks are employed to learn the significance of different item attributes. Experiments demonstrate that this method exhibits commendable recommendation performance on the Movielens1M, Hetrec-movielens, and Douban datasets compared to other models, with improvements in MAE, RMSE, and Accuracy of approximately 4.31%, 4.01%, and 3.70%, respectively.

As communication networks and smart gadgets evolve, researchers are becoming increasingly interested in recommender systems. Accurate click-through rate (CTR) prediction improves the performance of recommender systems. However, most current CTR prediction methods have problems in obtaining multi-level feature representations from user input, resulting in biased prediction outputs. Furthermore, CTR prediction models are frequently large-scale deep models, which limits their operational efficiency. To overcome these difficulties, this work introduces the Global Perception Federated Recommender System for Click-Through Rate Prediction (GPFed). The Global Perception Module, in particular, emphasizes the value of various field embeddings from a global viewpoint, focusing on the most salient intra-class features to improve multi-level feature representations in user data. Second, the Compact Tuning Module uses inner products to reduce model size and compression layers to minimize model parameters, resulting in increased operating efficiency. Furthermore, Device-Level Privacy Protection protects device privacy throughout the federated learning process. Experiments on three public datasets reveal that GPFed performs better and more efficiently. Compared to the best baseline models, GPFed improves performance by 10.85%, 3.72%, and 4.74% on the Criteo, Avazu, and MovieLens datasets, respectively.

The Graph Convolutional Network (GCN) is a powerful method for handling graph data in deep learning, which has found extensive application and exhibited outstanding performance in recommendation systems. Graph Contrastive Learning (GCL), on the other hand, is a self-supervised learning approach that learns valuable information about graph structures by contrasting representations of diverse elements within the graph. In the realm of recommendation systems, Graph Contrastive Learning has garnered significant research attention and interest.This paper introduces a novel framework for the Contrastive Learning model, termed Deeper Graph Contrastive Learning with Attention mechanism (ADGCL). To address the issue of excessive smoothing of nodes caused by the stacking of multiple layers of graph convolutional layers, resulting in reduced differences between nodes and subsequently impacting the performance of tasks such as node classification, this paper employs crosslayer connections and representation mapping. These techniques mitigate the oversmoothing phenomenon, enabling the network to delve deeper into learning. Furthermore, the incorporation of a self-attention mechanism for features enhances the efficiency of the network in handling information between different feature graphs, thereby improving model performance. The core idea revolves around enabling the model to adaptively learn the weights of feature graphs, better capturing crucial features to enhance overall performance. Experimental results demonstrate that our model outperforms current state-of-the-art methods, achieving a performance improvement of nearly 5% on the Yelp2018, Douban-Book, and Ml-1M datasets, respectively.

Knowledge graphs (KGs) have demonstrated exceptional effectiveness within the domain of recommendation systems.However, in recommendation systems, the high-quality representation of KG is hindered by noise and data sparsity.Additionally, user-item(UI) interactions dominate item node representations, with minimal influence from the knowledge graph.Furthermore, single-level contrastive learning (CL) inadequately captures implicit information in node embeddings.In order to address those issue, we propose the Hierarchy Knowledgeaware Contrastive Learning (HKCL) framework, which utilizes view enhancement in CL.Firstly, we employ an edge learner to reduce noise interference during the learning process by refining the representation of the graph.Secondly we encode the UI interaction graph and KG using Graph Neural Network (GNN).Finally, during the CL process, we use a finer-grained hierarchical CL method to enhance node representations, thereby discovering more potential features to alleviate the issue of data sparsity.Specifically, we conduct CL at the user-item level, item level, and entity-item level, making the CL more compatible with recommendation learning.Empirical findings from three extensive real-world datasets illustrate that our approach improves the accuracy of recommendation result.

Physical-world adversarial attacks based on universal adversarial patches have been proved to be able to mislead deep convolutional neural networks (CNNs), exposing the vulnerability of real-world visual classification systems based on CNNs. In this paper, we empirically reveal and mathematically explain that the universal adversarial patches usually lead to deep feature vectors with very large norms in popular CNNs. Inspired by this, we propose a simple yet effective defending approach using a new feature norm clipping (FNC) layer which is a differentiable module that can be flexibly inserted in different CNNs to adaptively suppress the generation of large norm deep feature vectors. FNC introduces no trainable parameter and only very low computational overhead. However, experiments on multiple datasets validate that it can effectively improve the robustness of different CNNs towards white-box universal patch attacks while maintaining a satisfactory recognition accuracy for clean samples.

We present a deep-learning architecture for multi-view stereo with conditional random fields (MVSCRF). Given an arbitrary number of input images, we first use a U-shape neural network to extract deep features incorporating both global and local information, and then build a 3D cost volume for the reference camera. Unlike previous learning based methods, we explicitly constraint the smoothness of depth maps by using conditional random fields (CRFs) after the stage of cost volume regularization. The CRFs module is implemented as recurrent neural networks so that the whole pipeline can be trained end-to-end. Our results show that the proposed pipeline outperforms previous state-of-the-arts on large-scale DTU dataset. We also achieve comparable results with state-of-the-art learning based methods on outdoor Tanks and Temples dataset without fine-tuning, which demonstrates our method's generalization ability.