The emerging 6G-IoT paradigm is driving communication toward intelligent services, semantic communication enables efficient semantic sharing via artificial intelligence (AI), significantly boosting communication efficiency. However, current semantic systems suffer from black-box decision-making, while existing explainable artificial intelligence (XAI) methods face two key challenges: explicability granularity mismatch and closed-loop optimization gap. To address these, we propose a semantic communication framework integrated with XAI (XAI-SCS). Specifically, we first design an explainable semantic codec architecture enhanced by Kolmogorov–Arnold Networks (KAN), where traditional fixed activation functions are replaced with learnable and parameterized ones, enabling function-level visualization to improve model explainability. Second, we develop an explainable semantic transmission module driven by contrastive learning that enhances the robustness of semantic transmission, and incorporating a semantic separability metric to quantify channel impacts on semantic integrity. Third, we introduced a KAN-enhanced causal semantic decoder, which integrates counterfactual interventions to generate pixel-level difference maps. We also propose a contrastive explanation consistency metric to evaluate the sensitivity of key features, enhancing the quality of the reconstruction. The experimental results show that our approaches enhance explainability across the entire decision process, achieve a significant accuracy improvement of up to 55% on the CIFAR-10 dataset with a bandwidth compression ratio of 1/25, and also obtain competitive image reconstruction quality in increasing compression levels. The source code is publicly available at: https://github.com/guyuangui/XAI-SCS.git.

This paper explores the issue of collision-free optimal tracking control of unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) formation under Byzantine attacks. Based on collision-free margin and collision risk angle, a collision avoidance scheme for UAV-UGV swarms is proposed, which takes into account both external and internal aspects of the formation. Meanwhile, considering that the UAV-UGV swarm is susceptible to the propagation of incorrect neighbors’ information and false input signals (called Byzantine attacks), which is effectively reinterpreted as the management of unknown variables within the control inputs. Then, a barrier function and a control force direction function are introduced in the optimal performance index for collision avoiding with each system’s radius, and a collision-free optimal control strategy under the reinforcement learning (RL) algorithm is investigated. Furthermore, the neural network is utilized to model the Byzantine attacks and appropriate unknown factors arising from Hamilton-Jacobi-Bellman (HJB) equation, the actor and critic adaptive laws are presented in actor-critic-identifier architecture. Subsequently, a collision-free optimal tracking control scheme is proposed to ensure safe collaborative moving of the UAV-UGV formation under Byzantine attacks. Finally, simulations are performed to validate the effectiveness of the proposed approach.

Advanced metering infrastructure in smart grids faces critical security challenges from distributed denial of service (DDoS) attacks, while traditional traceback methods suffer from resource constraints that limit their mitigation effectiveness. To address this, we propose a reverse auction-driven fog resource allocation strategy that innovatively employs a reverse auction mechanism to coordinate distributed fog resources for collaborative DDoS attack mitigation. Specifically, the framework is formulated as a 0–1 integer programming model, for which we develop a second-price sealed-bid auction (SPSA) algorithm that reduces computational complexity from factorial to polynomial time while maintaining allocation optimality. Experimental results demonstrate that the SPSA algorithm outperforms existing baseline methods, significantly improving both the efficiency and security of DDoS defenses in smart grid environments.

With the development of real-time video conferences, interactive multimedia services have proliferated, leading to a surge in traffic. Interactivity becomes one of the main features on future multimedia services, which brings a new challenge to Computer Vision (CV) for communications. In addition, many directions for CV in video, like recognition, understanding, saliency segmentation, coding, and so on, do not satisfy the demands of the multiple tasks of interactivity without integration. Meanwhile, with the rapid development of the foundation models, we apply task-oriented semantic communications to handle them. Therefore, we propose a novel framework, called Real-Time Video Conference with Foundation Model (RTVCFM), to satisfy the requirement of interactivity in the multimedia service. Firstly, at the transmitter, we perform the causal understanding and spatiotemporal decoupling on interactive videos, with the Video Time-Aware Large Language Model (VTimeLLM), Iterated Integrated Attributions (IIA) and Segment Anything Model 2 (SAM2), to accomplish the video semantic segmentation. Secondly, in the transmission, we propose a two-stage semantic transmission optimization driven by Channel State Information (CSI), which is also suitable for the weights of asymmetric semantic information in real-time video, so that we achieve a low bit rate and high semantic fidelity in the video transmission. Thirdly, at the receiver, RTVCFM provides multidimensional fusion with the whole semantic segmentation by using the Diffusion Model for Foreground Background Fusion (DMFBF), and then we reconstruct the video streams. Finally, the simulation result demonstrates that RTVCFM can achieve a compression ratio as high as 95.6%, while it guarantees high semantic similarity of 98.73% in Multi-Scale Structural Similarity Index Measure (MS-SSIM) and 98.35% in Structural Similarity (SSIM), which shows that the reconstructed video is relatively similar to the original video.

Green Internet of Things (Green IoT) is a technique that intends to reduce energy consumption and carbon emissions of Internet of Things (IoT) devices by optimizing hardware design, communication protocols, and data processing. One of the most promising schemes to realize Green IoT is personalized Federated Learning (pFL). Unfortunately, existing pFL methods still need further improvement in achieving Green IoT from the following aspects. 1) Computational energy consumption: model training on IoT devices generates a substantial amount of computational energy consumption. 2) Model performance: the dynamic role differences in each layer of the trained deep neural network need to be considered. Jointly considering these aspects, we present a novel pFL framework named Energy-Efficient personalized Federated Learning (EE-pFL) for establishing Green IoT. Specifically, an IoT device serves as an edge server. Each IoT device produces a customized model through a model training phase and a model aggregation phase. In the model training phase, a threshold-based sparsification strategy is introduced to reduce the computational energy consumption of IoT devices by selectively executing parameter updates. In the model aggregation phase, layer aggregation and an Adaptive Weight Calculation (AWC) mechanism are proposed to capture dynamic role differences in different layers of a deep neural network. Experimental results demonstrate that EEpFL shows lower computational energy consumption and higher classification accuracy than advanced benchmarks.

The extensive local training in Hierarchical Federated Learning (HFL) imposes a substantial computational energy burden on end devices, a problem intensified by inherent system and data heterogeneity. While prior works attempt to mitigate this by using heterogeneous models or adjusting local training, they often suffer from critical drawbacks such as accuracy degradation from update biases and an inability to adapt to the dynamic nature of device resources. This paper introduces FedE2AD (Federated Energy-Efficient Adaptive Distillation), a novel cloud-network collaboration framework that leverages dynamic distillation to holistically optimize the energy-accuracy balance. At its core, FedE2AD implements this collaboration through a multi-level optimization approach. At the cloud layer, a Dynamic Model Allocation strategy intelligently assigns model architectures by assessing device status from static, dynamic, and data-centric perspectives. At the device layer, Variable Local Iterations enable real-time adaptation to fluctuating computational power. Crucially, to counteract model divergence, FedE2AD employs a Dual Knowledge Sharing mechanism at the edge layer, which uniquely combines direct aggregation of shared structures with data-free model distillation to ensure robust knowledge transfer. Experiments conducted on simulation platforms show that FedE2AD markedly outperforms existing methods. For instance, on the CIFAR-10 dataset under strong heterogeneity, it reduces single-round computation energy by 21.1% and increases final model accuracy by 1.42% compared to HDHRFL.

Large Language Model (LLM)-driven multi-agent task planning offers a promising approach for automating complex missions, particularly in critical domains like disaster response. Nevertheless, the efficacy of existing planners is often compromised in dynamic environments. This vulnerability primarily stems from their inability to enforce complex, non-linear task dependencies, coupled with a lack of mechanisms to recover from execution failures. To address these gaps in reliability and resilience, this paper introduces ReFlex-LLM, a novel cloud-edge framework for autonomic multi-UAV task planning. ReFlex-LLM leverages a Directed Acyclic Graph (DAG) to explicitly model and guarantee the logical correctness of task sequencing, thereby preventing foundational planning errors. In parallel, ReFlex-LLM incorporates a closed-loop feedback mechanism, allowing the central planner to receive execution status from edge UAVs and to adapt the mission plan in response to unforeseen contingencies. Extensive simulations on complex, dependency-heavy tasks demonstrate that ReFlex-LLM significantly outperforms state-of-the-art baselines, boosting the overall mission success rate by over 20%.

Extensive local training in Hierarchical Federated Learning (HFL) imposes high energy demands on resourceconstrained devices, a problem exacerbated by system heterogeneity which also causes performance variability. To address this, we propose HFHEMS, a novel method utilizing heterogeneous models and distillation to improve energy efficiency in HFL. HFHEMS employs dynamic model allocation to tailor computational loads to device capabilities and uses variable local iterations for real-time training adjustment. To preserve model accuracy, it integrates a dual knowledge sharing strategy with data-free distillation, enhancing knowledge transfer. Experiments confirm HFHEMS significantly reduces computation energy while maintaining robust accuracy, thus achieving a superior energy-accuracy trade-off.

The wireless communication network, comprising billions of cloud, edge, and end devices, enables emerging applications such as the metaverse—an immersive virtual environment where experiences and user interactions converge. However, user interactions in the metaverse generate substantial amounts of private data (e.g., identity, location), raising significant privacy concerns despite their utility in training machine learning models. Artificial Intelligence of Things (AIoT) offers solutions to these challenges, with Federated Learning (FL) serving as a decentralized framework that enables collaborative model training without exposing local data. Nevertheless, deploying FL in large-scale environments like the metaverse increases vulnerability to malicious attacks. To address this issue, we propose a blockchain-based FL architecture that enhances trust and security. The architecture integrates a multi-task FL strategy with blockchain sharding to boost system throughput and reduce resource consumption. By partitioning the blockchain into smaller shards, we lower computational demands and enable concurrent training of multiple models, improving efficiency. We also design a shard creation algorithm based on bipartite matching and a bandwidth scheduling mechanism that prioritizes reliable devices with informative data. Experimental results show that our architecture outperforms existing baselines across multiple evaluation metrics.

Federated Learning (FL) can help multiple Internet of Things (IoT) devices to collaboratively train a machine learning model to provide intelligent services and applications (FL-AIoT). Due to IoT devices' limited storage capacity and energy, fresh data collected by devices often overwrites outdated data and establishes a heterogeneous data distribution. This causes the global model to forget outdated data's characteristics (i.e., catastrophic forgetting). Existing methods incorporate knowledge distillation into FL (i.e., federated distillation, FD) to extract and integrate characteristics from both fresh and outdated data, but they use fixed distillation temperatures for different devices, which overlooks that fixed distillation temperatures cannot match the heterogeneous data distribution on different devices and degrades global model accuracy. To this end, we propose a Federated Dynamic Decoupled Distillation method based on Logits distribution (Fed3DL). Specifically, Fed3DL utilizes decoupled distillation to mitigate catastrophic forgetting. To alleviate the impact of heterogeneous data distributions, Fed3DL novelly builds an adaptive temperature-aware mechanism to dynamically adjust the distillation temperature of each device based on the distribution of Logits. Additionally, Fed3DL introduces a regularization term into the local distillation loss to reduce inter-class characteristics disparity and improve model accuracy. Experiments on two datasets show that compared with the best of the 5 baselines, Fed3DL can improve the global model accuracy by an average of 3.40 %, reduce the forgetting rate by an average of 4.98 %, and achieve the lowest inter-class accuracy disparity.

Integrating vision and touch is key to understanding the physical world, but it faces two main challenges: effective multimodal fusion and high-fidelity tactile representation. This paper proposes a multimodal semantic communication framework based on foundation models through visual-tactile fusion. First, a multimodal enhancement fusion network extracts deep features from video to improve tactile recognition and semantic understanding. Second, a CLIP-driven framework, grounded in a tactile knowledge base, enhances the accuracy of tactile information transmission. An end-to-end model with joint source-channel coding further improves transmission efficiency. Finally, we introduce a tactile generative reconstruction method using ImageBind, which ensures high similarity in both visual features and pressure distribution. Experimental results confirm the effectiveness of our approach in semantic tactile reconstruction. Overall, the proposed method enables efficient, low-bit-rate communication with high semantic fidelity, offering a promising solution for visual-tactile fusion in real-world applications.

In real-world Industrial Internet of Things (IIoT) scenarios, due to the limited storage capacity of IIoT devices, fresh data continuously received by diverse devices will overwrite the outdated data and change the local data distribution. However, state-of-the-art studies have demonstrated that federated learning tends to focus on training with fresh data, and the latest global model may forget the historical update directions (i.e., catastrophic forgetting). This issue can significantly degrade the global model accuracy. Existing methods primarily focus on integrating outdated data characteristics into fresh data but overlook the large parameter update gap between global and local models during global aggregation. This gap can cause the global model updates to deviate from the optimal direction. To this end, we propose a federated adaptive weighted aggregation method based on model consistency (FedAWAC). Specifically, FedAWAC measures the model consistency on devices and dynamically adjusts the aggregation weights of each local model, thereby guiding the global model toward optimal updates. Furthermore, FedAWAC integrates$\mathcal {M}$historical global models most correlated to the latest global model on the cloud server to overcome catastrophic forgetting. Experiments on four different datasets (nonidentically and independently distributed settings) indicate that compared to five baselines, FedAWAC can improve global model accuracy by an average of 1.86%, reduce the forgetting rate by an average of 3.91%, and save average memory usage by up to 2.57 GB.

Cloud-centric deployment of large models brings privacy concerns and communication burdens to IoT devices. Federated Learning (FL) can help numerous IoT devices collaboratively train a large model in a distributed manner to provide intelligent services and applications (AIoT). However, due to the limited storage capacity of IoT devices, fresh data collected by IoT devices often overwrites outdated data and establishes Non-IID data distributions. Moreover, state-of-the-art studies have indicated that FL tends to use fresh data for model training. This causes the global model to forget outdated data’s characteristics (i.e., catastrophic forgetting). Some methods utilize Knowledge Distillation (KD) to extract and integrate characteristics from both fresh data and outdated data. However, existing KD-based methods use fixed distillation temperatures for different IoT devices, which overlooks that fixed distillation temperatures cannot match the data distributions on different IoT devices and may degrade global model accuracy. To this end, we propose a Federated Dynamic Decoupled Distillation method based on Logits distribution (Fed3DL). Specifically, Fed3DL utilizes decoupled distillation to mitigate catastrophic forgetting. To dynamically adjust the distillation temperature of each IoT device, Fed3DL novelly builds an adaptive temperature-aware mechanism based on the Logits distribution. Furthermore, Fed3DL introduces a regularization term into the local distillation loss to improve global model accuracy. Experiments on four datasets show that Fed3DL can improve the global model accuracy by an average of 3.19%, reduce the forgetting rate by an average of 4.46%, and achieve the lowest inter-class accuracy disparity.

Previous state-of-the-art studies have demonstrated that adversaries can access sensitive user data by membership inference attacks (MIAs) in Federated Learning (FL). Introducing differential privacy (DP) into the FL framework is an effective way to enhance the privacy of FL. Nevertheless, in differentially private federated learning (DP-FL), local gradients become excessively sparse in certain training rounds. Especially when training with low privacy budgets, there is a risk of introducing excessive noise into clients’ gradients. This issue can lead to a significant degradation in the accuracy of the global model. Thus, how to balance the user's privacy and global model accuracy becomes a challenge in DP-FL. To this end, we propose an approach, known as differential privacy federated aggregation, based on significant gradient protection (DP-FedASGP). DP-FedASGP can mitigate excessive noises by protecting significant gradients and accelerate the convergence of the global model by calculating dynamic aggregation weights for gradients. Experimental results show that DP-FedASGP achieves comparable privacy protection effects to DP-FedAvg and cpSGD (communication-private SGD based on gradient quantization) but outperforms DP-FedSNLC (sparse noise based on clipping losses and privacy budget costs) and FedSMP (sparsified model perturbation). Furthermore, the average global test accuracy of DP-FedASGP across four datasets and three models is about$2.62$%,$4.71$%,$0.45$%, and$0.19$% higher than the above methods, respectively. These improvements indicate that DP-FedASGP is a promising approach for balancing the privacy and accuracy of DP-FL.

The Intelligent Autonomous Transport System in 6G (6G-IATS) refers to the coordination of 6G, Artificial Intelligence (AI), and intelligent transportation systems, which is expected to revolutionize future intelligent transportation systems. In 6G-IATS, large-scale traffic flow prediction, affiliated with time series prediction, holds significant value for transportation planning and urban management. As an emerging AI method, Large Language Models (LLMs) have emerged prominently in time series forecasting. Unfortunately, it is challenging to achieve accurate and efficient large-scale traffic flow prediction by LLMs in 6G-IATS, due to the two issues: a) these LLMs fail to capture the spatio-temporal correlations in a large-scale road network, leading to limited prediction accuracy, and b) they process a substantial amount of training data on the central server, which imposes low training efficiency. Jointly considering the two concerns, this paper proposes a novel LLM and edge computing-based architecture for large-scale traffic flow prediction in 6G-IATS, called Spatio-Temporal Generative Large Language Model on Edge (STGLLM-E). In this architecture, we first decompose the entire large-scale road network into several subgraphs. To capture the spatio-temporal correlations, an LLM-based method named Spatio-Temporal Generative Large Language Model (STGLLM) including Spatio-Temporal Module (STM) and Generative Large Language Model (GLLM) is proposed. Secondly, to improve the training efficiency of the STGLLM-E, an edge training strategy based on edge servers is devised. Experiments are conducted on two real-world traffic flow datasets. The experimental results illustrate that the STGLLM-E is superior to the baselines in the prediction accuracy and the efficiency of training.

Traffic speed prediction is significant for intelligent navigation and congestion alleviation. However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i.e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor interpretability of traffic data with complicated spatio-temporal correlations, and 3) the latent pattern of traffic speed fluctuations over time, such as morning and evening rush. Jointly considering these factors, in this paper, we present a novel architecture for traffic speed prediction, calledInterpretable Causal Spatio-Temporal Diffusion Network(ICST-DNET). Specifically, ICST-DNET consists of three parts, namely the Spatio-Temporal Causality Learning (STCL), Causal Graph Generation (CGG), and Speed Fluctuation Pattern Recognition (SFPR) modules. First, to model the traffic diffusion within road networks, an STCL module is proposed to capture both the temporal causality on each individual road and the spatial causality in each road pair. The CGG module is then developed based on STCL to enhance the interpretability of the traffic diffusion procedure from the temporal and spatial perspectives. Specifically, a time causality matrix is generated to explain the temporal causality between each road’s historical and future traffic conditions. For spatial causality, we utilize causal graphs to visualize the diffusion process in road pairs. Finally, to adapt to traffic speed fluctuations in different scenarios, we design a personalized SFPR module to select the historical timesteps with strong influences for learning the pattern of traffic speed fluctuations. Extensive experimental results on two real-world traffic datasets prove that ICST-DNET can outperform all existing baselines, as evidenced by the higher prediction accuracy, ability to explain causality, and adaptability to different scenarios.

Cloud-enabled edge computing scenarios can intelligently cache and update the content on a periodic basis, thereby enhancing users' overall perception of quality, which is called quality of experience (QoE). To enhance the QoE, we aim to the multi-objective optimization, which maximizes the cache hit ratio while simultaneously minimizing traffic load and time latency. To address this issue, we focus on employing an innovative algorithm named HT-PAD, which provides a complete solution for prediction and decision-making for proactive caching. First, to improve the prediction accuracy of the cached content, we use the encoding layer in hyperdimensional computing to extract the information features. Second, HD-Transformer, as the prediction part of HT-PAD, is proposed to make predictions based on user preferences, historical information, and popular information. HD-Transformer uses DNN to predict user preferences and process time series data by combining hyperdimensional computation with Transformer. Third, to avoid error in the prediction content, we employ PER-MADDPG as the decision-making part of HT-PAD, which consists of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Prioritized Experience Replay (PER). We use MADDPG to enhance the content decision-making and utilized PER to select appropriate training samples for PER-MADDPG. Finally, our experiments have shown that our proposed approach achieves the strong performance in terms of the edge hit ratio, the latency, and the traffic load, thus improving the QoE

With the evolution of network infrastructure, the pattern of network traffic becomes unprecedentedly complex. Conventional machine learning algorithms struggle to cope with the high-dimensional data and real-time processing speeds required in such complex networks. Fortunately, Hyperdimensional Computing (HDC), which is power-efficient and supports parallel processing, provides a potential solution to this challenge. In this paper, we present TrafficHD, a novel classification framework that leverages HDC to analyze network traffic in real-time. By transforming network traffic features into high-dimensional binary vectors, TrafficHD enables the rapid execution of recognition tasks within the constraints of real-time systems. Extensive evaluations on a wide range of network tasks show that TrafficHD is 30.57× and 98.32× faster than state-of-the-art (SOTA) machine learning and HDC algorithms while providing 3× higher robustness to network noise.

The rapid development of deep learning technologies and the widespread deployment of sensing devices have brought considerable attention to Internet of Things (IoT). The smart sensing application is one of the popular applications in IoT. Personalized Federated Learning (pFL) is a replacement to traditional Federated Learning (FL) to tackle the statistical heterogeneity of clients’ private datasets (e.g., non-Independent and Identically (non-IID) data). However, existing pFL methods encounter two challenges in smart sensing applications: a) the global model preference, causing poor global model performance for minority classes on sensing device data. b) the dynamic role differences in each hierarchy of the layer-stacked deep learning model that needs to be considered. Jointly considering these challenges, we present a novel edge-cloud enabled pFL framework named pFL-Sensing for smart sensing applications. Specifically, the sensing device serves as an edge server. Each edge server produces a customized model through two phases: a model training phase and a model aggregation phase. In the model training phase, we design a novel loss function to alleviate the issue of the global model preference. In the model aggregation phase, hierarchical aggregation and an Adaptive Weight Calculation (AWC) mechanism are proposed to capture the dynamic role differences of model hierarchies. We perform simulation experiments on real image and text classification tasks. Experimental results show that pFL-Sensing demonstrates higher classification accuracy than advanced pFL baselines.

Inspection plays a vital role in dam operations, facilitating safety assurance and environmental monitoring. As an emerging technique, multi-Unmanned Aerial Vehicle (UAV) coordination technique brings a promising prospect to dam inspection, in which the core of the technique is UAVs Coverage Path Panning (UAVs-CPP), i.e., designing flight routes for UAVs to ensure comprehensive coverage of all point of interests within a dam region. Unfortunately, the dam region inevitably encounters natural disasters such as, the thunderstorm, strong wind, and earthquake, which is called abnormal environment. The abnormal environment triggers two issues: 1) the natural disasters leads to communication obstacles between UAVs due to communication link interruption and signal loss, and 2) the coverage and energy consumption of UAVs cannot be neglected. The existing coverage path planning algorithms are insufficient in the abnormal environment. To tackle these issue, a novel Deep Reinforcement Learning (DRL)-based architecture is devised for UAVs-CPP in the dam inspection under the abnormal environment, called Trace Pheromone Multi-Agent Deep Deterministic Policy Gradient (TP-MADDPG). Specifically, we first integrate a trace pheromone mechanism and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). The trace pheromone mechanism can simulate the natural pheromone, which promotes the inner indirect communication between UAVs. Furthermore, we employ a MADDPG to perform UAVs-CPP, in which a reward function is designed to conjointly optimize coverage and energy consumption. Finally, the experimental results demonstrate that TP-MADDPG shows significant UAVs-CPP performance contrasted with the advanced benchmarks.

In real Internet of Things (IoT) environments, IoT devices vary widely in data types and needs. IoT devices participating in Personalized Federated Learning (PFL) all have their own unique data characteristics, but there exist some similarities. Previous work enhances Personalized Local Models (PLMs)’ performance by clustering IoT devices’ PLM while ignoring dimensionality and communication volume, resulting in lower Global Model (GM) accuracy and PLMs’ performance. To this end, we propose a Personalized Federated Learning method based on Model Head Clustering (FedMHC). Specifically, FedMHC groups IoT devices with similar data characteristics and distributes different GMs to different device groups. FedMHC allows each IoT device to obtain a GM that best fits its local data characteristics and guides PLM training. FedMHC only clusters model head parameters on the server side. Thus, the edge server only needs to transmit the head parameters and a single shared feature extractor parameters during communication with IoT devices. The improvement can effectively address the issues of dimensionality and high communication volume. Experiments on CIFAR-100, Tiny-ImageNet, and AG News datasets demonstrate that FedMHC enhances the model accuracy by 1.79% and 5.9% in pathological heterogeneous scenarios, and by 1.43%, 0.92%, and 0.94% in practical heterogeneous scenarios, compared to the topperforming methods among 9 baselines.

The Internet of Medical Things (IoMT) stimulates the development of intelligent medical applications. As mental disorders become a global problem, emotion recognition has received widespread attention, as it can contribute to more comprehensive mental health monitoring and psychological assessment. Physiological signal-based emotion-aware monitoring is a particularly promising application due to its noninvasive and objective data collection. Recently, multimodal emotion recognition has been enhanced with wireless body area network (WBAN) access to IoMT, where wireless medical sensors are interconnected and abundant signals are acquired conveniently. However, how to synthesize these multisource physiological signals to facilitate emotion recognition is a challenging problem due to their heterogeneity and interference. To solve this problem, we propose a real-time differential multimodal transformer (Diff-MT), where the main components are the differential hyperinformation extraction (DHE) module, the multimodal global cross-attention encoder (MGCE), and the difference-augmented feature fusion (DFF). Ultimately, we endow the system with emotional awareness and distribute the state to IoMT devices. Extensive experiments demonstrate that the proposed Diff-MT exhibits superior performance compared to existing methods on the WESAD and DEAP datasets and is appropriate for IoMT-based healthcare.

Proactive caching in 6 G cloud-edge collaboration scenarios, intelligently and periodically updating the cached contents, can either alleviate the traffic congestion of backhaul link and edge cooperative link or bring multimedia services to mobile users. To further improve the network performance of 6 G cloud-edge, we consider the issue of multi-objective joint optimization,i.e., maximizing edge hit ratio while minimizing content access latency and traffic cost. To solve this complex problem, we focus on the distributed deep reinforcement learning (DRL)-based method for proactive caching, including content prediction and content decision-making. Specifically, since the prior information of user requests is seldom available practically in the current time period, a novel method named temporal convolution sequence network (TCSN) based on the temporal convolution network (TCN) and attention model is used to improve the accuracy of content prediction. Furthermore, according to the value of content prediction, the distributional deep Q network (DDQN) seeks to build a distribution model on returns to optimize the policy of content decision-making. The generative adversarial network (GAN) is adapted in a distributed fashion, emphasizing learning the data distribution and generating compelling data across multiple nodes. In addition, the prioritized experience replay (PER) is helpful to learn from the mosteffectivesample. So we propose a multivariate fusion algorithm called PG-DDQN. Finally, faced with such a complex scenario, a distributed learning architecture,i.e., multi-agent learning architecture is efficiently used to learn DRL-based methods in a manner of centralized training and distributed inference. The experiments prove that our proposal achieves satisfactory performance in terms of edge hit ratio, traffic cost and content access latency.

Non-linear activation function plays an essential role in neural networks (NNs) for their generalization ability. However, deploying the intricate mathematical operations on hardware platforms like Field-Programmable Gate Array (FPGA) turns out a great challenge. Prior works based on piecewise functions or look-up table (LUT) either involve complex manual operations or neglect hardware overhead. To this end, this paper proposes a simple yet scalable and effective approximation called DIF-LUT, which is applicable to various non-linear functions. Specifically, the proposed method can achieve accurate approximation by piecewise linear matching to fit the function derivative roughly and range addressable LUT to offset the difference. Moreover, self-adaptive mechanisms are applied to automatically minimize hardware cost in terms of different accuracies. The experiments show that compared to state-of-the-art methods, DIF-LUT costs 43.68% fewer LUTs and 70.8% fewer flip-flops (FFs) without any digital signal processor (DSP), while achieving 2.7x approximation accuracy at 554.1MHz on Xilinx Zynq UltraScale+.

In federated learning with differential privacy, an obvious phenomenon of local gradient sparsification emerges in some training rounds. When training with low privacy budgets, there is a risk of excessive noise being introduced into the uploaded gradients, leading to a significant decrease in the accuracy of the global model. To tackle the trade-off between privacy protection and model accuracy with low privacy budgets, we propose a differential privacy federated aggregation method based on gradient sparsification (DP-FedAGS), which not only prevents excessive noise addition by protecting only significant gradients, but also accelerates global model convergence by dynamically calculating the weight of the gradient. Experimental results indicate that DP-FedAGS achieves comparable privacy protection to DP-FedAvg and cpSGD, while outperforming DPFedSNLC. Moreover, our approach respectively attains an approximate average test accuracy improvement of $2 .45 \%, 4 . 79$% and $0 . 29$% over the above three methods, rendering DP-FedAGS a promising approach for exploring a balance between privacy protection and model accuracy.

In the era of Information and Communication, big data is rapidly generated due to the increasing data-driven applications in Internet of Things (IoT). Effectively processing such data, e.g., knowledge learning, on resource-limited IoT becomes a challenge. In this paper, we introduce a digital twin-enabled IoT, in order to achieve hyper-connected experience, green communication, and sustainable computing. Although knowledge learning benefits from the proposed system, system latency and energy consumption are still our focus in the distributed learning architecture. To this end, we leverage Deep Reinforcement Learning (DRL) to present the deep deterministic policy gradient with double actors and double critics (D4PG) to manage the multi-dimensional resources, i.e., CPU cycles, DT models, and communication bandwidths, enhancing the exploration ability and improving the inaccurate value estimation of agents in continuous action spaces. Extensive experimental results prove that the proposed architecture can efficiently conduct knowledge learning, and our intelligent scheme can effectively improve the system efficiency.

Industrial Internet of Things (IIoT) is experiencing rapid developments in the era of Industry 4.0. However, the ever-increasing applications put forward higher requirements for authentication. Facing such a problem, researchers combine two cutting-edge techniques, i.e., physical unclonable function (PUF) and blockchain. In detail, PUF can generate multiple challenge–response pairs (CRPs) for IIoT devices by leveraging their unique physical features. Moreover, blockchain platforms are employed for storing/synchronizing CRPs, thereby resisting the single-point failure. Although realizing the unclonable authentications, the existing works ignore the device heterogeneity of IIoT and fail to develop the specified blockchain platform for supporting PUF. In this article, we present a hybrid-PUF-based consortium blockchain for IIoT authentication, named HPCchain. Specifically, we first present the notion of hybrid-PUF, which assigns different devices to generate different types of PUFs, and then employs them to play different roles in HPCchain. In this way, we can overcome the IIoT heterogeneity. Moreover, we propose the PUF-empowered credit scheme for HPCchain and realize the dynamic endorsement with which we develop a PUF-based consensus mechanism for HPCchain. Finally, we design the registration and authentication schemes for IIoT nodes, atop HPCchain. Extensive experiments demonstrate the validity of our proposals.

With the advent of the Internet of Things (IoT) era and 5G, ubiquitous sensing devices (e.g., smartphones, surveillance sites, and security cameras) have been widely used in various fields, resulting in the generation of a huge amount of monitoring data. The rise of federated learning makes it possible to leverage monitoring data to train deep neural networks through cloud-edge collaboration without compromising privacy. However, the non identically and independently distributed (called Non-IID) data collected by IoT devieces creates a client drift phenomenon, resulting in a slow convergence of the global model. To this end, we propose a new Federated learning framework based on Gradient Variance Reduction with a correction weight control mechanism and Global gradient descent with Momentum, named FedGVRGM to conduct gradient correction and reduce the negative impacts of prediction parameters. Specifically, in the local training phase, FedGVRGM combines gradient variance reduction with a correction weight control mechanism to further correct the local model parameters, thus reducing the dispersion of model parameters among clients. In the global aggregation phase, FedGVRGM integrates the historical change states of the global model through the gradient descent with momentum to reduce the oscillations and improve the convergence speed of the global model. We refer to the above methods of gradient adjustment in the local and global training phases as FedGVR and FedGM, respectively. Numerous evaluations are conducted on CIFAR-100, CIFAR-10, and MNIST datasets to prove that FedGVRGM has a faster convergence rate than other stateof-the-art approaches such as Federated Averaging (FedAvg), FedProx, FedReg, FedGVR, and FedGM.

In edge networks, distributed computing resources have been widely utilized to collaboratively perform a machine learning task by multiple nodes. However, the model training time in heterogeneous edge networks is becoming longer because of excessive computation and delay caused by slow nodes, namely, stragglers. The parameter server even abandons stragglers which fail to return the outcome within a reasonable deadline, called straggler dropout, decreasing the model accuracy. To optimize the computation cost and maintain the model accuracy, we focus on mitigating the heavy computation of stragglers and preventing straggler dropout. Therefore, we propose a novel scheme named dynamic grouping and heterogeneity-aware gradient coding (DGH-GC) to tolerate stragglers by employing dynamic grouping and gradient coding. DGH-GC evenly distributes stragglers in each group and encodes gradients based on their computation capacity to prevent them drop out. However, DGH-GC exacerbates the communication burden by making data duplication to tolerate stragglers. Relying on the scheme, we further propose an algorithm called DGH-(GC)2 to compress transferred gradients in both upstream communication and downstream communication. Experimental evaluations prove that DGH-(GC) outperforms all state-of-the-art methods and DGH-(GC)2 further speeds up the convergence time of the trained model and saves about 26% average iteration time compared to the DGH-(GC).

In the transportation industry, task offloading services of edge-enabled Internet of Vehicles (IoV) are expected to provide vehicles with the better Quality of Experience (QoE). However, the various status of diverse edge servers and vehicles, as well as varying vehicular offloading modes, make a challenge of task offloading service. Therefore, to enhance the satisfaction of QoE, we first introduce a novel QoE model. Specifically, the emerging QoE model restricted by the energy consumption: 1) intelligent vehicles equipped with caching spaces and computing units may work as carriers; 2) various computational and caching capacities of edge servers can empower the offloading; and 3) unpredictable routings of the vehicles and edge servers can lead to diverse information transmission. We then propose an improved deep reinforcement learning (DRL) algorithm named PS-DDPG with the prioritized experience replay (PER) and the stochastic weight averaging (SWA) mechanisms based on deep deterministic policy gradients (DDPG) to seek an optimal offloading mode, saving energy consumption. Specifically, the PER scheme is proposed to enhance the availability of the experience replay buffer, thus accelerating the training. Moreover, reducing the noise in the training process and thus stabilizing the rewards, the SWA scheme is introduced to average weights. Extensive experiments certify the better performance, i.e., stability and convergence, of our PS-DDPG algorithm compared to existing work. Moreover, the experiments indicate that the QoE value can be improved by the proposed algorithm.

In the transportation industry, task offloading services of edge intelligent Internet of Vehicles (IoV) are expected to provide vehicles with the better Quality of Experience (QoE). However, the various status of diverse edge servers and vehicles, as well as varying vehicular offloading modes, make a challenge of task offloading service. Therefore, to enhance the satisfaction of QoE, we first introduce a novel QoE model. Specifically, the emerging QoE model restricted by the energy consumption, (1) intelligent vehicles equipped with caching spaces and computing units may work as carriers; (2) various computational and caching capacities of edge servers can empower the offloading; (3) unpredictable routings of the vehicles and edge servers can lead to diverse information transmission. We then propose an improved deep reinforcement learning (DRL) algorithm named RA-DDPG with the prioritized experience replay (PER) and the stochastic weight averaging (SWA) mechanisms based on deep deterministic policy gradients (DDPG) to seek an optimal offloading mode, saving energy consumption. Extensive experiments certify the better performance, i.e., stability and convergence, of our RA-DDPG algorithm compared to existing work. Moreover, the experiments indicate that the QoE value can be improved by the proposed algorithm.

In edge-enabled Internet of Things (IoT), computation offloading service is expected to offer users with better Quality of Experience (QoE) than traditional IoT. Unfortunately, the growing multiple tasks from users are occuring with the emergence of the IoT environment. Meanwhile, the current computation offloading with QoE is solved by deep reinforcement learning (DRL) with the issue of instability and slow convergence. Therefore, improving the QoE in edge-enabled IoT is still the ultimate challenge. In this article, to enhance the QoE, we propose a new QoE model to study the computation offloading. Specifically, the emerged QoE model can capture three influential elements: 1) service latency determined by local computing latency and transmission latency; 2) energy consumption according to local calculation and transmission consumption; and 3) task success rate based on the coding error probability. Moreover, we improve the deep deterministic policy gradients (DDPG) algorithm and propose a algorithm named the double-dueling-deterministic policy gradients (D3PG) based on the proposed model. Specifically, the actor network highly relies on the critic network, which makes the performance of the DDPG sensitive to the critic and thus leads to poor stability and slow convergence in the computation offloading process. To solve this, we redesign the critic network by using Double Q -learning and Dueling networks. Extensive experiments verify the better stability and faster convergence of our proposed algorithm than existing methods. In addition, experiments also indicate that our proposed algorithm can improve the QoE performance.

In the era of information, the services of fog computing environment with content delivery are expected to offer users the better satisfaction of Quality-of- Experience (QoE) than that in a conventional environment. Nevertheless, the dataflow and new demands from users increase along with the promising of content-centric computing system in fog computing environment. Therefore, the satisfaction of QoE will become the major challenge. In this article, to enhance the satisfaction of QoE, we propose QoE models to evaluate the quality of service in fog computing environment concerning both system and users. The value of QoE does not only refer to the system cost, but also the Mean Opinion Score (MOS) of users. Therefore, our models could capture influential factors from system cost based on system states and services for users. Specially, we mainly focus on issues of cache allocation and transmission rate. Under this fog computing environment, aiming to the capacity of cache allocation among fog nodes and handle transmission rates under a constrained total system cost and MOS, we devote our efforts to the following two aspects. First, we formulate the QoE as a joint resource allocation problem under different transmission rates to acquire best QoE. Then, we propose a dynamic algorithm based on shortest path tree (SPT), which is suitable for fog computing environment with content delivery frequently. Simulation results reveal that the benefit for using the dynamic allocation (DA) method to allocate resource can achieve high QoE performance.

In the age of big data, the services in pervasive edge environment are expected to offer end-users better Quality of Experience (QoE) than that in a normal edge environment. Nevertheless, various types of edge devices with storage, delivery, and sensing are coming into our environment and produce the high-dimensional big data accompanied by a volume of pervasive big data increasingly with a lot of redundancy. Therefore, the satisfaction of QoE becomes the primary challenge in high dimensional big data on the basis of pervasive edge environment. In this paper, we first propose a QoE model to evaluate the quality of service in pervasive edge environment. The value of QoE does not only include the accurate data, but also the transmission rate. Then, on the basis of the accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on Deep Learning, which is suitable for pervasive edge environment with high-dimensional big data analysis. Simulation results reveal that our proposals could achieve high QoE performance.