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.

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.

With the fast development of AI technologies, deep learning is widely applied for biomedical data analytics and digital healthcare. However, there remain gaps between AI-aided diagnosis and real-world healthcare demands. For example, hemodynamic parameters of the middle cerebral artery (MCA) have significant clinical value for diagnosing adverse perinatal results. Nevertheless, the current measurement procedure is tedious for sonographers. To reduce the workload of sonographers, we propose MCAS-GP, a deep learning-empowered framework that tackles the Middle Cerebral Artery Segmentation and Gate Proposition. MCAS-GP can automatically segment the region of the MCA and detect the corresponding position of the gate in the procedure of fetal MCA Doppler assessment. In MCAS-GP, a novel learnable atrous spatial pyramid pooling (LASPP) module is designed to adaptively learn multi-scale features. We also propose a novel evaluation metric, Affiliation Index, for measuring the effectiveness of the position of the output gate. To evaluate our proposed MCAS-GP, we build a large-scale MCA dataset, collaborating with the International Peace Maternity and Child Health Hospital of China welfare institute (IPMCH). Extensive experiments on the MCA dataset and two other public surgical datasets demonstrate that MCAS-GP can achieve considerable performance improvement in both accuracy and inference time.

In Internet applications, network conversation is the primary communication between the user and server. The server needs to efficiently and quickly return the corresponding service according to the conversation sent by the user to improve the users’ Quality of Service. Thus, Conversation Information Seeking (CIS) research has become a hot topic today. In Cloud Computing (CC), a central service mode, the conversation is transmitted between the user and the remote cloud over a long distance. With the explosive growth of Internet applications, network congestion, long-distance communication, and single point of failure have brought new challenges to the centralized service mode. People put forward Edge Cloud Computing (ECC) to meet the new challenges of the centralized service mode of CC. As a distributed service mode, ECC is an extension of CC. By migrating services from the remote cloud to the network edge closer to users, ECC can solve the above challenges in CC well. In ECC, people solve the problem of CIS through edge caching. The current research focuses on designing the edge cache strategy to achieve more predictable caching. In this article, we propose an edge cache placement method Evolutionary Game based Caching Placement Strategy (EG-CPS). This method consists of three modules: the user preference prediction module, the content popularity calculation module, and the cache placement decision module. To maximize the predictability of the cache strategy, we are committed to optimizing the cache hit rate and service latency. The simulation experiment compares the proposed strategy with several other cache strategies. The experimental results illustrate that EG-CPS can reduce up to 2.4% of the original average content request latency, increase the average direct cache hit rate by 1.7%, and increase the average edge cache hit rate by 3.3%.

Key frame sampling is a common component in video tasks. Putting more effort into key frames, rather than processing all frames equally, can significantly reduce computational costs and improve processing efficiency. This paper presents ORSampler, an adaptive Online Residual-based key frame Sampler. ORSampler relies on feature residuals to sample key frames and decouples from subsequent video tasks. To facilitate ORSampler, a self-coached mechanism is designed to speed up learning, and an adaptive multi-level feature fusion is proposed to fit the diversity of subsequent video tasks. OR-Sampler has a fast inference speed and can work online. Extensive experiments on two typical video tasks verify the effectiveness and generality of our proposed ORSampler.

With the arrival of the Internet of Things (IoT) era, multi-tier computing has attracted significant attention. The multi-tier computing can organize many computing devices and provide sufficient computing resources to support various IoT applications. However, due to the complex architecture and the dynamic system status of the multi-tier computing network, task offloading for multi-tier computing is still challenging. This paper proposes a novel task offloading method named OSTTD, to deal with the Offloading of Splittable Tasks with Topological Dependence in multi-tier computing networks. OSTTD formulates the task offloading as a sequential decision-making problem and learns the task offloading policy by Deep Reinforcement Learning (DRL). Compared with existing task offloading approaches, OSTTD is the first method in which the topological dependence among sub-tasks of the splittable task is fully considered. OSTTD makes offloading decisions intelligently based on the dynamic system status and can be applied to various multi-tier network topology structures. To verify the effectiveness of OSTTD, we extend and build a latency-aware multi-tier computing simulation platform. Extensive simulations show that OSTTD can significantly reduce the task processing time, thus, improving the overall task processing efficiency in multi-tier computing networks.