While multi-device cooperative task-oriented semantic communication (TOSC) enhances task performance through comprehensive information representation, it inevitably introduces redundancy, thereby increasing communication overhead. Existing redundancy elimination methods suffer from limitations in interpretability and coarse granularity, hindering the optimal utilization of communication resources. To this end, we propose a disentangled information bottleneck guided TOSC framework (DisenIB-TOSC). The framework first employs the basic IB for initial feature compression, then formulates a novel DisenIB specifically designed for multi-device cooperation inference, which enhances task performance and achieves interpretable feature disentanglement by separating features into common and private components, thereby establishing a theoretical foundation for redundancy identification. Subsequently, we derive differentiable, computationally tractable forms for both IB objectives by combining variational approximation, consistency constraints, and density ratio trick. Leveraging the disentangled features, we further design a feature importance-aware selective transmission strategy, DisenIB-TOSC-ST, which quantifies feature importance via mutual information estimation to dynamically discriminate and control redundant feature transmission. Experimental results on several tasks demonstrate that our method outperforms baselines in task performance while reducing communication costs, verifying the effectiveness and interpretability of feature disentanglement.

Intelligent search enables users to access information from the Internet quickly, but existing schemes fail to achieve accurate semantic awareness and reliable information transmission, especially in constrained communication conditions, which degrade search accuracy and personalized user experience. To address these challenges, we propose a multiuser semantic communication system to perform personalized search (PS) tasks, named MU-SemCom-PS. In particular, the system introduces a novel semantic encoder at the transmitter to deeply extract user-specific search semantics by analyzing search history from multiple perspectives, and designs a semantic decoder at the receiver to recover and enhance search semantics by leveraging implicit correlations among users, thus the PS tasks are performed based on the recovered search semantics. To optimize the PS tasks for all users, the federated learning (FL) framework is leveraged to jointly train the MU-SemCom-PS system through knowledge collaboration and sharing. Experimental results show that the proposed scheme significantly improves search accuracy and robustness under constrained communication conditions.

Demand-oriented data service can provide the physical entity information for Internet of Things (IoT) users conveniently and quickly. The traditional cloud-oriented data service architecture has been inapplicable to the state time-varying and privacy-sensitive entity data in IoT due to long response delay and the risk of privacy leakage for the entities and users, respectively. The edge-based architecture lacks global service function although it can alleviate problems with cloud services. Moreover, existing demand-oriented data service ignores the characteristics of “thousands of people have thousands of faces” and the implicit intents of users, which results in limited service quality and weak user experience. To solve the above problems, a personalized secure demand-oriented data service scheme is proposed. Specifically, an edge-cloud collaborative architecture is designed to realize privacy-preserving, timely response, and personalized search combining the advantages of edge and cloud. To achieve the personalized service for IoT users, a time span fused personalized ranking method (TSFPR) is proposed to deeply perceive individual demands via mining user preferences with temporal evolution characteristics. Finally, an edge-cloud collaborative personalized secure data service approach (ECPSS) oriented different search modes is presented to achieve encryption data matching and personalized reranking, thereby improving the service quality of the IoT system synthetically. Security analysis and simulation demonstrate the effectiveness of the proposed method in terms of privacy preserving, data service time, and personalized performance.

Caching AI of Things (AIoT) data at the edge can reduce the load on cloud centers while providing real-time services for AIoT users. Existing static caching strategies based on popularity prediction fail to meet users’ demands for time-varying entity data, while dynamic caching strategies focus only on evaluating the time-varying state characteristics of entity data, but ignore the differences in popularity among entities, resulting in poor service experience. To this end, a device–edge collaborative differentiated data caching strategy considering static entity popularity as well as dynamic system state is proposed. First, a hot entity recognition method centered on user interests is designed to achieve individual preference estimation by mining users’ long short-term interests, and then achieve group interest prediction based on social computing. Based on this, a dynamic caching optimization method is designed, which considers the timeliness of entity data and communication cost of the system to design the objective function of optimal cache decision and then solve it based on reinforcement learning. Simulation results demonstrate that the proposed caching strategy achieves better performance than other benchmark strategies in terms of cache hit rate and search cost.