Object detection through deep learning techniques plays a pivotal role in various industrial applications, such as defect detection. With industries increasingly recognizing the importance of protecting sensitive data, there is a growing interest in collaborative detector training using federated learning (FL). However, existing FL solutions face challenges in effectively addressing object detection tasks with limited labeled data across practical institutions. This challenge is especially pronounced in scenarios with densely packed objects, where obtaining sufficient labels is time consuming and costly. In this article, we present an innovative federated semi-supervised learning (SSL) framework expressly designed for object detection in densely packed scenes (FSSLOD) to overcome above challenges. To achieve this, our approach leverages a teacher-student network on the client side for local SSL and employs a designed consistency loss to align the output of the teacher network with that of the student network. Furthermore, we present an elastic update mechanism to mitigate the intricate issue of data distribution disparity by preventing the inclusion of inadequately trained knowledge into the shared model. Comprehensive evaluations on two real-world object detection data sets demonstrate that the proposed method significantly enhances object detection performance in densely packed scenes while also ensuring data privacy.

Lightweight models with high accuracy is critical for edge intelligence. Although the Knowledge Distillation (KD) has been successfully applied to reduce the accuracy loss of quantized neural networks, especially for resource-constrained edge devices, the process of pre-training complex high-precision teacher networks in KD however, will bring huge training overhead. Recently proposed online distillation frameworks offer a good solution for teacher-free distillation, but the regularization effect and simple average aggregation of KD further weaken the representation capability of quantized models that have been reconstructed. In this work, we propose Precision-Mixed and Weight-Average Ensemble (PMWAE) consisting of multiple group members and a group leader. PMWAE provides additional knowledge by changing the bit-precision of the activation and generates aggregated weights for each member in group by attention-based mechanism. The ensemble knowledge is further passed to the group leader to obtain the final model. Extensive experiments on the CIFAR-10/100 and ImageNet-1K datasets show that our method outperforms the existing state-of-the-art methods, both on standard convolutions and depth-wise separable convolutions.

In the Industrial Internet of Things (IIoT), model and computing power sharing among devices can improve resource utilization and work efficiency. However, data privacy and security issues hinder the sharing process. Besides, in the process of model sharing, due to the customization of industrial equipment functions and the high separation of model and task types between devices, it is difficult to share model and optimize models among devices with different task requirements. In this article, we propose an adaptively federated multitask learning (AFL) for IIoT devices efficiently model sharing. Inspired by the parameter sharing mechanism, AFL builds a sparse sharing structure by designing an iterative pruning network and generating subnets for each task. Moreover, for better share relevant information, we further propose tailored task mask layers for effectively training specialized subnets, and an adaptive loss function to dynamically adjust the priority between tasks. Extensive experiments show that AFL can successfully fit hundreds of tasks from different devices into one model, which preserves both high accuracy and system scalability, and outperforms other related approaches that naively combine federated learning with multitask learning.

Blockchain as a new decentralized chain structure can be used in edge computing to solve the security issue caused by edge nodes in model training. However, large amounts of data exchanges in the process of model training of edge computing reduce the performance of blockchain, and meanwhile, the block needed to be saved in the edge node challenges storage capacity of the edge node. Therefore, in the paper we propose a safe and efficient model training mechanism based on onchain and offchain collaboration. In the mechanism, edge nodes train models locally, store the model parameters in offchain and only return identifiers for model aggregation. By the method, the storage pressure of the edge node is reduced and the efficiency of executing consensus algorithms are increased. Moreover, in the mechanism we design a reputation evaluation model based on confidence factors to avoid the uploading of random and wrong data of edge nodes. Evaluation results show that our schemes can reduce the average delay and resources consumption, increase transaction throughput and maintain security compared with a state-of-the-art scheme.

As things currently stand, the blockchain industry is siloed among many different platforms and protocols resulting in various islands of blockchains. Restrictions regarding assets transfers and data migration between different blockchains reduce the usability and comfort of users, and hinder novel developments within the blockchain ecosystem. Interoperability will be the main topics of next-generation blockchain technologies. In this paper, we focus on how to enable interoperability between two heterogeneous blockchains in the context of data migration. We first build an cross-chain data migration architecture based on data migration oracle. Second, we design a data migration mechanism based on former architecture. By employing the proposed data migration architecture, it is equivalent to opening a secure channel between two heterogeneous blockchains allowing secure data migration. By applying data migration mechanism, the confidentiality, integrity and security of migrated data can be well guaranteed.

The introduction of edge computing (EC) in intelligent driving allows the vehicle to offload tasks to the EC server closer to the vehicle side, creating a new paradigm for task offloading and resource allocation. The movement of the vehicle, the time sensitivity of the processing data, and the resource allocation of the EC server have become bottlenecks of the rapid development of intelligent driving. In this article, we jointly considered the problems of the network economy and resource allocation. In order to eliminate dependence on third parties, we propose a resource transaction architecture based on the blockchain. Moreover, we propose the dynamic allocation algorithm of edge resources (DAERs) based on the double auction mechanism to maximize the satisfaction of users and service providers of edge computing (SPs), where the DAER algorithm is implemented in the form of smart contracts in the blockchain architecture. In particular, we propose the state search algorithm that can improve the prediction accuracy of the staged destination of the vehicle to help allocate resources reasonably. Through simulation experiments, we verify the superior performance of the DAER algorithm in terms of resource utilization rate and the satisfaction of both parties participating in the auction.

Task offloading has been a hot topic in the field of edge computing. Resources fairness of edge computing servers which is the destination of task offloading directly impacts life of server and the process quality of task. In this paper, we propose a subtask-virtual machine mapping model (subtask-VM mapping model) to complete task offloading from the terminals to the servers. Considering the reasonable allocation of server resources, we also propose stack-based cache mechanism (SCM) to ensure the fairness of server resources allocation. We transform the problem of mapping model solution into the problem of optimal matching in the bipartite graph, and verify the performance of our algorithm by contrast experiment. In particular, the fair performance of our algorithm for server-side is over 84%.