With the rapid development of 6G network technology and intelligent transportation system (ITS), the edge deployment and lightweight of network applications for modern users have gradually become a possibility. In this work, we propose a mobile intelligent vehicular task offloading method efficient mobile edge computing assisted task offloading using generative adversarial network (MEGAN) based on task representation learning and federated optimization for lightweight task recognition and energy consumption optimization of mobile edge computing (MEC) in 5G/6G transportation networks. The extended dataset of tasks is constructed based on generative adversarial network (GAN) to overcome the problems of data model overfitting and sample imbalance. A task preprocessing model between the edge server and the mobile users is established by using the federated deep learning with the knowledge distillation. The task classification accuracy, energy consumption of signal transmission and data computing are the optimization objectives to realize the MEC and lightweight application deployment. Experimental results show that compared with other state-of-the-art vehicular task offloading methods, the MEGAN method has great potential to promote the efficiency and energy consumption optimization of new transportation service processing in the future.

Federated Learning enables multiple clients to collaboratively train a model without sharing their individual data, thereby protecting local data privacy. However, attackers, such as untrusted servers, can still compromise the privacy of clients’ local training data through various inference attacks. One feasible approach to protect client privacy during training is the incorporation of differential privacy. Nevertheless, achieving an ideal level of privacy protection with differential privacy often degrades the model’s performance, significantly reducing its accuracy. To enhance model accuracy while minimizing the additional client heterogeneity introduced by differential privacy, this paper proposes a method that integrates personalized layers and data transformations, FusTP-FL. The core of our FusTP-FL is the incorporation of personalized layers and personalized data transformations within the client’s local training model, which further reduces client heterogeneity and improves model accuracy. We evaluated the model’s accuracy on six common datasets; experimental results demonstrate that the proposed FusTP-FL effectively enhances model accuracy across two different differential privacy modes (CDP and LDP), increasing it by up to 45%. Furthermore, we show that compared to PRIVATEFL, our method achieves lower client heterogeneity.

An emerging Internet of Video Things (IoVT) application, crowd counting is a computer vision task where the number of heads in a crowded scene is estimated. In recent years, it has attracted increasing attention from academia and industry because of its great potential value in public safety and urban planning. However, it has become a challenge to cross the gap between the increasingly heavy and complex network architecture widely used for the pursuit of counting with high accuracy and the constrained computing and storage resources in the edge computing environment. To address this issue, an effective lightweight crowd counting method based on an encoder–decoder network, named lightweight crowd counting network (LEDCrowdNet), is proposed to achieve an optimal tradeoff between counting performance and running speed for edge applications of IoVT. In particular, an improved MobileViT module as an encoder is designed to extract global-local crowd features of various scales. The decoder is composed of the adaptive multiscale large kernel attention module (AMLKA) and the lightweight counting atrous spatial pyramid pooling process module (LC-ASPP), which can perform end-to-end training to obtain the final density map. The proposed LEDCrowdNet is suitable for deployment on two edge computing platforms (NVIDIA Jetson Xavier NX and Coral Edge TPU) to reduce the number of floating point operations (FLOPs) without a significant drop in accuracy. Extensive experiments on five mainstream benchmarks (ShanghaiTech Part_A/B, UCF_CC_50, UCF-QNRF, WorldExpo’10, and RSOC data sets) verify the correctness and efficiency of our method.

The rapid development of current data centers and Industrial Internet of Things has brought about the explosive growth of information, which leads to the need for better performance of cluster communication systems. Coflow scheduling has the potential to enhance communication performance among applications in data parallel clusters. However, current coflow scheduling techniques that lack preliminary knowledge often depend on a fixed set of threshold parameters within a multilevel feedback queue (MLFQ), disregarding network variability. Though manually tweaking threshold settings may support network flexibility, it negatively impacts real-time performance and increases workload. Furthermore, manual threshold adjustments often fail to react promptly and adaptively to network environmental changes. To address the issues highlighted above, this paper presents D-MLFQ, a novel approach that leverages deep reinforcement learning to dynamically and autonomously regulate the threshold of MLFQ. As a result, D-MLFQ offers enhanced scheduling and communication optimization capabilities. Furthermore, the study performs trace-driven simulations to assess the efficacy of D-MLFQ. Empirical data indicate that D-MLFQ outperforms Aalo, which utilizes fixed threshold, by up to 1.39× in terms of coflow’s completion time. Compared to other common scheduling algorithms such as per-flow fairness, D-MLFQ achieves up to 2.13× faster completion time.

The swift advancement of distributed computing has enhanced the support for big data and massive-scale models. Yet, delivering superior services to manage large and intricate network flows in data center networks remains a formidable challenge. In this paper, we present ReQ-tank, an intricate flow scheduling approach based on a multi-level feedback queue (MLFQ) devised to achieve flow prioritization and efficient flow scheduling. ReQ-tank employs a two-tier scheduling strategy: On the flow scheduling layer, priority queues are segmented into two categories, and the flows within high-priority queues follow a strict priority scheduling, while those in low-priority queues adhere to differential weighted Round-robin scheduling; On the packet scheduling layer, ReQ-tank modifies the priority of initially high-priority re-transmitted packets to facilitate fine-grained data packet scheduling. We carry out simulation experiments on web search workloads and data mining workloads. Experimental results demonstrate that ReQ-tank can curtail packet wait time in the network, significantly truncate the flow completion time (FCT) of delay-sensitive flows and counteract the issue of flow starvation in traditional strict priority queues. Consequently, ReQ-tank is deemed more suitable for complex distributed network applications.

Federated Learning has been widely used due to its ability to train models while ensuring data privacy and security. However, the presence of non-i.i.d. (independent and identically distributed) data among different participating entities leads to significant performance disparities in Federated Learning. In addition, as the essence of Federated Learning involves model training on local devices without centralized data storage, it poses certain challenges in personalized tasks. Traditional centralized machine learning methods can perform deep learning and personalized model training on centralized data, while distributed data in Federated Learning may not provide enough information to support personalized requirements. In this paper, we propose a personalized Federated Learning method called FedFC, which adopts parameter decoupling to address the data domain shift issue in Federated Learning. We experimentally validate our proposed solutions on the OrganCMNIST and COVID-19 datasets. The experimental results show that compared to existing methods, FedFC achieves average accuracy improvements of 4.93% and 6.31% on the two datasets, respectively. Additionally, by employing partial gradient uploading, we successfully reduce the communication overhead by 22.67% and 26.91% for each dataset.

Industrial Internet of Things (IIoT) is the leading application scenario of the fifth generation wireless communication systems (5G) and beyond. Nonorthogonal multiple access (NOMA) has become a key technology for 5G due to its high spectrum efficiency. In this article, a joint channel and power resource allocation problem is investigated for cross-cell IIoT networks with aim of maximizing sum rate of NOMA-based machine-to-machine pairs and cellular Machine Devices (cMDs). Since joint channel and power resource allocation problem is an NP-hard problem, the original problem is transformed into a hypergraph model to optimize channel and power resource allocation. Then, a channel allocation algorithm based on hypergraph coloring theory is proposed, and an alternative power allocation algorithm is presented. Next, some properties of hypergraph coloring and complexities are analyzed. Finally, simulation results demonstrate that the proposed algorithm outperforms the graph-based algorithm in terms of sum rate, and also improves the spectrum efficiency significantly.

In recent decades, remote sensing object counting has attracted increasing attention from academia and industry due to its potential benefits in urban traffic, public safety, and road planning. However, this issue is becoming a challenge for computer vision because of various technical barriers, such as large-scale variation, complex background interference, and nonuniform density distribution. Recent results show hopeful prospects for object counting using convolutional neural networks (CNNs), but most existing CNN-based methods draw on larger and more complex architectures, which leads to a huge computational and storage burdens, severely limiting their application in real-world scenarios. In this article, a lightweight multiscale feature fusion network for remote sensing object counting, named LMSFFNet, is presented to achieve a better balance between the running speed of the network and the counting accuracy. Specifically, in the encoding process, we select a MobileViT module as the backbone of the network to reduce the numbers of network parameters and computing cost. In return, a cascade structure of the channel–spatial attention mechanisms compensates for the weaker feature extraction ability of the lightweight network. In the decoding process, a lightweight multiscale context fusion module (LMCFM) as a multiscale feature fusion module is developed to solve the problem that the number of parameters increases with the expansion of the object scale when extracting multiscale features. In addition, a lightweight counting scale pooling module (LCSPM) is used to mine the subtle features of the target object. Two kinds of typical object counting experiments, namely, experiments on remote sensing benchmarks (RSOC dataset) and crowd benchmarks (ShanghaiTech, UCF-QNRF, and UCF_CC_50 datasets), show the effectiveness of the proposed method.

Researchers and practitioners are exploiting Remote Direct Memory Access (RDMA) technology to improve the efficiency of distributed machine learning and meet the demands of data-center applications. RDMA requires lossless network link to fully unleash its power. RDMA Over Converged Ethernet (RoCE) v2 focuses on congestion control, but fails to achieve efficient packet loss recovery; Improved RoCE NIC (IRN) addresses this issue based on RoCEv2, but does not use the Priority-based Flow Control (PFC) to maintain the advantage of RoCEv2 in detecting congestion. This paper proposes a method of congestion detection and link control via feedback in RDMA transmission, namely Feedback Data Flow Control (FDFC), that does not rely on PFC. FDFC detects and controls the link condition in real time to achieve the goals of precise detection, congestion control, and efficient packet loss recovering.

Federated learning enables local nodes to train a global model together by uploading only training updates to the parameter server without exchanging private data. However, as the complexity of the federated learning task increases, the communication volume of the training process becomes extremely large, hence the huge communication traffic becomes a serious bottleneck in current federated learning application. Existing methods reduce communication overhead from two aspects, the number of communications and the traffic per communication. But these methods usually lead to more consumption of computing resources or a decrease in model accuracy. To handle these problems, this paper proposes a data fitting based weight compression algorithm, FWC, which includes four sequential stages: sparsification, polynomial fitting, encoding, reconstruction and two mechanism: warm-up and accumulation. In particular, the warm-up mechanism can well address the problem of slow convergence in early training period. Experimental results on models with different scales show that FWC is able to provide more than 600x traffic compression at the cost of only millisecond-level computational time cost and less than 1% accuracy loss.

Physical practice with one hand results in performance gains of the other (un-practiced) hand in a unilateral motor task. Yet how it induces performance gains of interlimb coordination in the bimanual movements between trained limb and the opposite, untrained limb is unclear. The present study designed a game-like interactive system for physical practice, in which an avatar’s hands could be controlled itself or by the subject during a bimanual movement task in an immersive virtual reality environment. Participants practiced with the bimanual task by simultaneously drawing non-symmetric three-sided squares (e.g., U and C) to learn limb coordination with the following training strategies: (1) performing and seeing a bimanual task (BH-BH); (2) performing a unimanual task with right hand and seeing a bimanual action (RH-BH); (3) not performing a task but seeing a bimanual action (noH-BH); (4) performing and seeing a unimanual task (RH-RH). We found that the learning performance was better after BH-BH and RH-BH compared with other training strategies. In addition, we examined the effects of virtual hand representations on the learning performance after RH-BH. We found that the performance after training was increased with the realism level of virtual hands. These findings suggest that the proposed approach of RH-BH with realistic virtual hand would result in transfer of coordination skill to the unpracticed hand, which puts forward a new approach for learning and rehabilitation of coordination skill in patients with unilateral motor deficit in immersive environments.