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.

Federated Semi-Supervised Learning (FSSL) has emerged as a crucial topic in medical image analysis, allowing multiple medical institutions to collaboratively train a global model using limited labeled data. However, existing FSSL methods focus solely on an effective combination of federated learning and semi-supervised learning, ignoring the heterogeneity of client data and the inadaptability of semi-supervised methods in diverse environments, which leads to knowledge bias in local models and impedes stable convergence. To this end, we explore the application of personalization in FSSL and propose a novel dual-calibrated co-training framework. To adapt to the unique feature distribution of client data, we consider collaborative relationships among clients to aggregate a personalized model for each client. We further build a dual-student architecture with the personalized model and private local model on the client side, which encourages model disagreement for co-training while enhancing participant privacy. Most importantly, we design dual calibration strategies that adaptively optimize the model: Local calibration improves the boundary discrimination of the local model by dynamically replacing pseudo-label boundary patches; Global calibration corrects model direction based on the real-time perception of the biases between local dual-student models. Experimental results show the effectiveness of our method on a private medical dataset and two public medical datasets.

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.

While metabolic imaging can facilitate early diagnosis by revealing physiological changes of lesions, it is limited by high cost, high radiation risk, and potential renal impairment. Thus, developing an effective approach for Anatomical-to-Metabolic Image Synthesis (A2MIS) is highly required. However, existing methods are heavily hindered by the gap between distinct domains, and fail to provide a confidence score for the synthesized images, severely restricting their clinical applications. Here, we propose a novel Residual-guided Latent Brownian-bridge Co-Diffusion (RLBCD) model for A2MIS. Specifically, RLBCD starts with a co-diffusion process that leverages a residual diffusion branch to capture inter-domain differences, which are injected into an enhanced diffusion branch to maximally reconstruct modality-specific details. Furthermore, to explore desired residual guidance, we investigate the encoder and decoder features in diffusion models, and accordingly design a Hybrid-Granularity Fusion to integrate consistent semantics and complementary information for fine-grained reconstruction. Additionally, a latent consistency score is developed to enhance the restoration of modality-specific information, which also serves as an indicator of the inherent confidence of the synthesized images. Extensive experiments conducted on five public and in-house datasets demonstrate that RLBCD not only outperforms state-of-the-art methods for A2MIS, but also is valuable for downstream clinic applications.

Whole Slide Images (WSIs) are paramount in the medical field, with extensive applications in disease diagnosis and treatment. Recently, many deep-learning methods have been used to classify WSIs. However, these methods are inadequate for accurately analyzing WSIs as they treat regions in WSIs as isolated entities and ignore contextual information. To address this challenge, we propose a novel Dual-Granularity Cooperative Diffusion Model (DCDiff) for the precise classification of WSIs. Specifically, we first design a cooperative forward and reverse diffusion strategy, utilizing fine-granularity and coarse-granularity to regulate each diffusion step and gradually improve context awareness. To exchange information between granularities, we propose a coupled U-Net for dual-granularity denoising, which efficiently integrates dual-granularity consistency information using the designed Fine- and Coarse-granularity Cooperative Aware (FCCA) model. Ultimately, the cooperative diffusion features extracted by DCDiff can achieve cross-sample perception from the reconstructed distribution of training samples. Experiments on three public WSI datasets show that the proposed method can achieve superior performance over state-of-the-art methods. The code is available at https://github.com/hemo0826/DCDiff.