As a structured representation of real-world facts, knowledge graphs (KGs) play a vital role in IoT applications, due to their strong reasoning capabilities and interpretability. However, private user IoT KG data often needs to be centrally collected for embedding training, which poses significant privacy risks and limits the scalability of knowledge-driven downstream applications in distributed IoT environments. Federated learning (FL) has emerged as a promising solution for decentralized model training, eliminating the need for direct data collection. However, existing federated knowledge graph embedding (KGE) methods often struggle to preserve the inherent graph structure of entities and relations, leading to fragmented and incomplete representations. Additionally, they struggle to effectively capture diverse relational dependencies within personal KGs. To address these challenges, this article proposes an enhanced federated KG embedding method for personal knowledge sharing (FPKS) to enable privacy-preserving KGE training. The FPKS framework consists of a central server and multiple federated clients. To enhance entity and relationship alignment across clients, FPKS maintains separate embedding tables for entities and relationships on the server. Moreover, to capture the structural and contextual information of personal KGs, we introduce a local encoder-decoder architecture that employs a graph convolutional network (GCN) variant as an encoder and a KGE scoring function as a decoder. Furthermore, we propose a bidirectional composite operator for GCN (BiDGCN) to enhance multi-relational information aggregation. Extensive experiments on two widely used KG datasets demonstrate that FPKS significantly outperforms existing methods, improving the quality of learned embeddings while ensuring data privacy. Our approach facilitates decentralized personal knowledge sharing, marking an advancement in secure and efficient IoT knowledge-driven services.

Knowledge graph (KG)-based service recommendation methods address issues such as data sparsity and cold start in real-world service recommendations by integrating external knowledge as auxiliary information. Recently, large language models (LLMs) have gained significant attention due to their powerful comprehension and reasoning capabilities. LLM-based recommendation systems also demonstrate advantages in interpretability and few-shot service reasoning. However, the integration of LLMs and KGs into existing service recommendation methods presents two major challenges: (1) the difficulty of aligning service recommendation tasks with language modeling tasks, and (2) the lack of interpretable quantification of the relationship between knowledge and personalized preferences. To address these challenges, this paper proposes WPKL (Weighted Preference Optimization based on KG and LLM). WPKL leverages external knowledge to assist LLMs in modeling user preferences and employs a hybrid graph neural network (GNN) framework to enhance preference representation. Additionally, a weighted preference optimization (WPO) approach is proposed to fine-tune the LLM, enabling interpretable quantification of user preferences and personalized knowledge. Extensive experimental results demonstrate that WPKL achieves high-quality service recommendations.

To address the limitations of existing microservice bad smell (MBS) detection and refactoring tools, particularly the lack of fully automated architectural bad smell refactoring solutions, this paper proposes a MAPE-K-based self-adaptive framework for microservice bad smell refactoring (SABER). The framework aims to eliminate architectural smells through closed-loop self-repair, thereby reducing risks related to main-tainability, scalability, and security. SABER employs a cloud-edge collaborative architecture: edge-side components collect real-time metrics from a Kubernetes cluster, while cloud-side components detect architectural smells and dynamically generate refactoring strategies. These strategies include service merging, splitting, adding, and adjustment. By automatically executing these strategies, SABER achieves architectural bad smell elimi-nation. Experimental results show that the framework achieves 95.53 % precision and 84.71 % recall across ten benchmark systems, significantly improving refactoring efficiency compared to semi-automated and manual methods. Its deep integration with DevOps pipelines validates its effectiveness in sustaining microservice health, offering a novel paradigm for autonomous maintenance in distributed systems.

In the era of industrial internet, achieving a dynamic balance between mass production and personalized customization has become a core demand for industrial development. This necessitates that product service systems can accurately capture users' personalized requirements. Although large language models (LLMs) possess powerful dialogue and reasoning capabilities, enabling them to identify implicit requirements, they still exhibit limitations in supply-demand matching, particularly in the precise alignment between personalized requirements and product capabilities. To this end, this study innovatively proposes a personalized product customization service framework (Req2Sol) based on fine-grained supply-demand cognition. This framework formalizes the modeling of supply-demand capabilities and finegrained personalized requirements through a knowledge graph (KG), integrating them into the LLM training process. This significantly enhances the model's understanding of supplydemand relationships, enabling accurate product configuration and customization recommendations. Firstly, a multi-view modeling approach for supply-demand capabilities and personalized fine-grained requirements is proposed, constructing a requirementproduct knowledge graph. Secondly, the knowledge graph is utilized as pre-training data to achieve domain-specific finetuning of LLMs. By introducing conditional scenarios and strategies, a five-level quantitative evaluation system for Req2Sol is established, improving its performance by 7.3 % compared to the baseline model when handling unconventional or inaccurately expressed user requirements. Finally, using the air conditioning domain as a case study, the effectiveness of the framework in achieving precise supply-demand cognition and customized product recommendations is validated through the Req2Sol-QAS system developed by invoking Req2Sol services.

The integration of Mobile Edge Computing (MEC) into the Industrial Internet of Things (IIoT) has markedly improved resource accessibility and propelled digital-intelligent advancements. Nevertheless, the substantial costs of MEC infrastructure also pose critical challenges to incentive mechanism design. Specifically, there is still an absence of a standardized and widely recognized incentive framework for the dynamic and non-cooperative interactions between edge service requesters and providers. Furthermore, the highly complicated characteristics of IIoT necessitate a greater reliance on dependable and trustworthy edge resource provision than other paradigms, which implies that human-centric factors (e.g., credit) are equally crucial as profit-driven metrics (e.g., price) in incentive design. To tackle these challenges, we propose In3Edge, a Stackelberg game-based incentive mechanism that systematically considers the interplay between profit-driven and interest-oriented indicators while accommodating heterogeneous peers, subjective interest divergences, and objective resource disparities. Particularly, leveraging convex optimization theory, we provide rigorous proofs and in-depth analyses of the intrinsic properties of In3Edge, encompassing the concavity/convexity of utility functions, equilibrium solution boundaries, optimal responses under peer/interest heterogeneity, and closed-form solutions for symmetric multipeer scenarios while articulating a series of propositions and theorems to underpin future research. Finally, extensive experiments are constructed under diverse dynamically changing scenarios with distinct characteristics, confirming the strong motivational capabilities of In3Edge in MEC-empowered IIoT.

The predominant obstacles confronting contemporary cyber-physical systems (CPSs) are their extensive heterogeneity and stringent constraints, such as diverse applications and real-time service requirements. While the incorporation of mobile edge computing could alleviate some of these constraints, challenges persist in equilibrating services and loads due to the finite computational resources of edge servers. Concurrently, the implementation of associated modules or methodologies has been prompted by escalating apprehensions regarding service security, which may have a detrimental impact on the performance of CPS, particularly in terms of resource occupation and service overhead. To this end, we develop an end-edge-cloud-collaborative CPS framework in which tasks are modeled as workflow applications, followed by constructing a multi-stage service-enhanced method named SerFlow. In SerFlow, a relevance-aware security precaution mechanism is devised, which evaluates the connection hierarchy and correlation metrics across subtasks, subsequently establishing the service anti-conflict mechanism to augment security levels. Particularly, a comprehensive evaluation score for security precaution levels is provided, which enables the security precaution to engage in the following optimization operations and evaluation modules as a quantifiable metric. Leveraging the non-Euclidean geometry framework, we then develop a Pareto frontier modelling method that integrates Newton-Raphson and geodesic while a survival value evaluation strategy with correlation constraints is involved in accomplishing coarse-grained cluster selection. Subsequently, an improved value-based and model-free deep reinforcement learning algorithm is suggested to generate fine-grained service strategies in end-edge-cloud collaborative scenarios. Finally, comprehensive experiments demonstrate the effectiveness of SerFlow in achieving enhanced security precaution levels while maintaining superior service efficiency.

In recent years, the development of online social network platforms has led to increased research efforts in social recommendation systems. Unlike traditional recommendation systems, social recommendation systems utilize both user-item interactions and user-user social relations to recommend relevant items, taking into account social homophily and social influence. Graph neural network (GNN)-based social recommendation methods have been proposed to model these item interactions and social relations effectively. However, existing GNN-based methods rely on centralized training, which raises privacy concerns and faces challenges in data collection due to regulations and privacy restrictions. Federated learning has emerged as a privacy-preserving alternative. Combining federated learning with GNN-based methods for social recommendation can leverage their respective advantages, but it also introduces new challenges: (1) existing federated recommendation systems often lack the capability to process heterogeneous data, such as user-item interactions and social relations; (2) due to the sparsity of data distributed across different clients, capturing the higher-order relationship information among users becomes challenging and is often overlooked by most federated recommendation systems. To overcome these challenges, we propose a federated social recommendation approach with enhanced hypergraph neural network (HGNN). We introduce HGNN to learn user and item embeddings in federated recommendation systems, leveraging the hypergraph structure to address the heterogeneity of data. Based on carefully crafted triangular motifs, we merge user and item nodes to construct hypergraphs on local clients, capturing specific triangular relations. Multiple HGNN channels are used to encode different categories of high-order relations, and an attention mechanism is applied to aggregate the embedded information from these channels. Our experiments on real-world social recommendation datasets demonstrate the effectiveness of the proposed approach. Extensive experiment results on three publicly available datasets validate the effectiveness of the proposed method.

The diversified and personalized service matching provided by the metaverse is hindered by challenges such as sparse interaction data, the long-tail effect, and the fragmentation of user demand profiles. Knowledge graph (KG)-based service recommendation methods alleviate these issues by incorporating external knowledge related to service items. However, their effectiveness is often compromised by irrelevant KG entities and relations. This irrelevant knowledge noise leads to two core chal lenges: (1) incomplete encoding of service knowledge into user and item embeddings, and (2) the absence of reliable true noise labels for distinguishing low-relevance knowledge information. To overcome these challenges, we propose MSKD, a metaverseser vice recommendation framework based on knowledge denoising. MSKD first constructs a user preference KG by fusing service item external knowledge with interaction data, then employs an adaptive noise pruning module to eliminate irrelevant entities. Subsequently, it constructs distinct Interaction and Preference KG views and aligns their embeddings within a shared repre sentation space through contrastive learning, thereby drawing semantically related items closer. To further remove noise without ground-truth labels, a KG bottleneck mechanism optimizes the information flow, retaining only knowledge most relevant to the recommendation task. Finally, a multi-task learning strategy jointly optimizes recommendation loss, contrastive alignment, and denoising objectives. Extensive experiments on three public service recommendation datasets show consistent improvements in top-K metrics (e.g., an average Recall@K increase of nearly 6.55% on Alibaba-iFashion dataset), demonstrating MSKD's effectiveness in denoising and its impact on enhancing metaverse service recommendations.

Knowledge graph completion (KGC) tasks have been developed to address the inherent incompleteness of KGs. Recently, knowledge graph embedding (KGE) methods have gained popularity for embedding entities and relations, proving effective in KGC. However, privacy concerns make it challenging to collect privacy KG data from different institutions in the actual application. Federated learning has emerged as a solution for training models with decentralized data, eliminating the need for collecting private data. However, existing federated KGE methods overlook the implicit graph structural information of entities and relations, resulting in fragmented and incomplete representations within federated clients. Moreover, these methods often struggle with capturing multiple relational representations. To address these challenges, we propose a Federated Graph to Embedding (FedGE) approach based on encoder-decoder to capture interactions among entities and relations. Extensive experiments on two common KG datasets demonstrate the superiority of our method. The code is available at https://github.com/s460305450/FedGE.git.

In the context of dynamic service ecosystems, the inability of conventional knowledge graph embedding (KGE) methods to efficiently update incremental knowledge poses a significant challenge for the effectiveness of intelligent web applications. To address the continuous updating challenges of service knowledge, this paper introduces MetaHG, a meta-learning strategy for KGE. Unlike existing meta-learning KGE studies that focus solely on local entity information, MetaHG incorporates both local and potential global structural information from current snapshot’s seen knowledge graphs (KGs) to mitigate issues such as spatial deformation and enhance the representation of unseen entities. Our approach initializes entity embeddings using ‘in’ and ‘out’ relationship matrices and refines them through a hybrid graph neural network (GNN) framework, which includes a GNN layer for local information and a hypergraph neural network (HGNN) layer for potential global information. The meta-learning strategy embedded in MetaHG effectively transfers meta-knowledge for the accurate representation of emerging entities. Extensive experiments are conducted on a self-collected clothing industry service dataset and two publicly available open-source KG datasets. By comparing with several baselines, experiment results demonstrate the superior performance of MetaHG in generating high-quality embeddings for emerging entities and dynamically updating service knowledge.