In multimedia applications, Federated Learning (FL) has emerged as an effective training paradigm, enabling distributed clients to collaboratively train a shared model without transmitting raw data. However, FL remains vulnerable to privacy threats such as data reconstruction and membership inference attacks, which has motivated the adoption of secure aggregation protocols to protect client model updates. Existing secure aggregation schemes that combine homomorphic encryption with secret sharing predominantly follow a sharing-decryption paradigm, requiring additional client interaction during decryption and thereby incurring substantial computational, synchronization, and communication overhead. In this paper, we propose Threshold Vector Aggregation (TVA), a novel secure aggregation protocol that adopts a sharing-encryption and aggregation paradigm. Under TVA, each client encrypts its local update only once using a unique private key and uploads a single ciphertext to the server, without any further interaction. The cipher-texts are directly aggregatable yet individually undecryptable, and only the final aggregated result can be correctly recovered by the server. Extensive experiments demonstrate that TVA significantly outperforms state-of-the-art secure aggregation schemes, achieving up to 33× lower server-side aggregation time, 132× faster client-side encryption, and 170× reduction in communication overhead, while preserving model accuracy comparable to plaintext federated learning.

ABSTRACT Objectives In rapidly evolving industrial environments, resource management in Mobile Edge Computing (MEC) has gained increasing attention, aiming to ensure Quality of Service (QoS) for Artificial Intelligence of Things (AIoT) applications. While MEC reduces end‐to‐end delay, tasks offloaded to the cloud still encounter bottlenecks when processing massive AIoT‐generated data streams. To overcome this, we introduce a Collaborative Edge‐Edge (CE2) architecture that integrates heterogeneous edge servers and devices, enabling real‐time latency‐energy trade‐offs and accelerating AI‐driven decision‐making at the network edge. Managing resources in such dynamic, multi‐task, multi‐server environments remains challenging, especially under variable task‐arrival rates. Methods To tackle this, we propose LyDRM, a hybrid dynamic resource management scheme that synergistically combines model‐based optimization with model‐free deep reinforcement learning (DRL). A Lyapunov optimization module is embedded to enforce queue‐stability constraints, ensuring bounded task backlogs over time. Result To validate its effectiveness, extensive simulations show that LyDRM reduces the average weighted system cost‐defined as a combination of latency and energy metrics‐by at least 39.89%, significantly lowers both latency and energy consumption, accelerates convergence, and maintains long‐term stability in dynamic AIoT scenarios.

The rapid integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has led to the pro liferation of AIoT applications, significantly escalating demands for computing and communication resources in multi-user, multitask scenarios. A critical challenge lies in efficiently managing resource allocation to ensure Quality of Service (QoS) for diverse Deep Neural Network (DNN) inference tasks. Existing edge cloud collaborative inference approaches partially address this by hierarchical resource management; however, these methods often overlook the joint optimization of computation, communication, and data quality, and neglect long-term system stability in dynamic environments. To address these limitations, we propose EdgeManager, an online adaptive resource management frame work for hierarchical DNN inference in collaborative heterogeneous edge environments. Specifically, we formulate a Mixed Integer Nonlinear Programming (MINLP) optimization problem aimed at balancing inference accuracy and latency. Leveraging Lyapunov optimization, we transform the complex, multi-stage dynamic optimization problem into manageable deterministic sub-problems for each time slot, ensuring long-term stability. Furthermore, we introduce HyDRL-MO, a hybrid approach integrating model-free Deep Reinforcement Learning (DRL) and model-based multi-decision optimization techniques to achieve efficient and stable resource allocation. Extensive experimental evaluations demonstrate that EdgeManager significantly improves system performance, achieving up to 42.08% enhancement in average system benefits compared to state-of-the-art solutions.

Federated Semi-Supervised Learning (FSSL) offers a distributed learning paradigm that addresses the critical issue of label scarcity while preserving client privacy. However, current FSSL methods are often hindered by an over-reliance on labeled data for initialization, high communication overhead, and suboptimal global model performance in heterogeneous data settings. To overcome these limitations, we propose pFSSL-D, a novel Dual-Phase Generalization and Personalization Pipeline designed to generate several models for unlabeled clients. In the first phase, decentralized contrastive learning with feature alignment is proposed to efficiently pre-train a robust and generalizable feature extraction model while minimizing communication overhead. In the personalization phase, we introduce a parameter-granularity federated fine-tuning approach with semantic consistency, which decouples model parameters into general and personalized components, providing specialized update strategies for each component. This method effectively balances global generalization with client-specific adaptation, ensuring robustness in heterogeneous environments. Extensive evaluations on benchmark datasets show that pFSSL-D consistently outperforms state-of-the-art FSSL methods in terms of accuracy, convergence speed, and resource overhead.

Person re-identification (ReID) in distributed surveillance networks presents significant networking challenges, particularly in coordinating multiple edge devices for real-time processing. While cloud-based solutions offer powerful computational capabilities, they introduce substantial network latency, hindering real-time performance in multi-camera indoor environments. To address these challenges, we present a distributed edge computing architecture that enables real-time person reidentification. Our system introduces two key networking innovations: (1) an$N$-frame feature matching mechanism that enhances identification accuracy at network edges, and (2) a dynamic load balancing framework that efficiently distributes processing tasks across edge nodes when network congestion occurs. As a foundation for this research, we introduce BNBUMTMC, a mixed dataset comprising both images and videos from multiple cameras, specifically tailored for indoor MTMCReID scenarios. Through extensive deployment in a university building environment with 10 cameras and multiple edge devices, we demonstrate that our system achieves high identification accuracy while significantly reducing network transmission latency compared to cloud-based approaches. Our experience provides practical insights into designing and implementing distributed edge computing systems for real-time surveillance applications.

The Internet of Vehicles offers a comprehensive perception of environment, which enhance transportation efficiency. To handle the large amount of collected data, distributed edge intelligence is a promising paradigm in which the edge server share data and computing resources with each other, providing low-latency services for local devices. However, offloading the computing-intensive application fully to one edge server might lead to a large latency as the computing resource of edge servers are usually limited. To solve this problem and elevate Quality of Service (QoS) to new heights, existing methodologies merely partition applications into modules, overlooking the crucial fact that these modules harbor distinct input requirements, posing a pivotal challenge in scheduling optimization. In this article, we study dependent task offloading by partitioning applications and dividing modules into two categories: 1) stateful modules and 2) statelss modules. The stateful modules necessitate the incorporation of previous calculation results, while stateless modules operate independently. We subsequently frame this intricate dependent task offloading challenge as an optimization problem, boldly acknowledging its NP-hard nature. Considering this, we unveil an innovative online collaborative dependent task offloading (OCDTO) algorithm, grounded in a two-layer collaborative edge computing architecture. This algorithm meticulously minimizes the make-span, redefining the benchmarks for efficiency. Our rigorous experimentation not only validates but also showcases the superiority of our approach, consistently achieving the lowest average system cost compared to the state-of-the-art, which verifies the effectiveness of our proposed approach in latency-sensitive and computing-intensive scenarios.

The integration of edge computing with deep neural networks (DNNs) is crucial for intelligent industrial cyber-physical systems. Typically, deploying DNNs on heterogeneous edge devices relies on methods like model compression and partitioning. However, these approaches often result in homogeneous models across devices. This homogeneity limits the collective capability of edge computing systems, particularly in terms of generalization to diverse data distributions and adaptation to dynamic industrial environments. In this work, we propose to treat each DNN on an edge device as an independent model, aggregating their capabilities via ensemble learning to enhance generalization and dynamic adaptability. To realize this, we introduce the Adaptive Ensemble Knowledge Distillation Framework (AEKDF), combining cloud-based model training with edge computing based collaborative inference. In the cloud, AEKDF develops an enhanced Born Again Network that generates diverse, lightweight models tailored to specific edge devices through knowledge distillation. This process ensures model diversity which is critical to effective ensemble learning. On the edge, AEKDF employs an adaptive ensemble technique that aggregates prediction logits across devices, enabling rapid adaptation to changing environments and maintaining inference efficiency. Our extensive evaluations conducted on a realistic prototype demonstrate the substantial boost in predictive performance achieved by our AEKDF, showcasing a 4% to 10% accuracy improvement on the CIFAR-100 compared to conventional single-model approaches, while maintaining low latency.

In the era of Big Data, artificial intelligence and information science are the key technologies to extract the value of data and enhance the competitiveness of enterprises. The characteristics of distributed, small-scale, and sparse lead to the isolated data island problem. To solve these problems, Federated Learning is proposed. However, a large number of terminal models need to be uploaded to the server in Federated Learning, especially for the actual scenario of Internet of Things. Therefore, huge communication costs are required which dramatically increases the pressure on the backbone network. Furthermore, the low quality of the local model will lead to decreased accuracy and convergence rates of the model. To overcome the above limitations, we propose heterogeneous device collaboration based federated learning (HDCFL), which constructs a three-layer structure for Federated Learning by leveraging edge computing and designs a heterogeneous device collaboration method that groups the terminals based on their computing power, communication time, and data volume to train the model. Then, we conduct a theoretical analysis of the proposed algorithm which verifies its advantage. At last, the experimental result demonstrates that the proposed algorithm consistently achieves superior performance in terms of both convergence speed and accuracy compared with state-of-the-art baselines.

In the fast-developing industrial environments, extensive focus on resource management within Mobile Edge Computing (MEC) aims to ensure low-latency QoS, however, some tasks offloaded to the cloud still experience high latency. Additionally, high energy consumption, poor link reliability, and excessive processing delays are intolerable for industrial applications. Compared to general servers, edge computing devices based on Arm architecture exhibit lower latency and higher energy efficiency. This highlights the need for improved heterogeneous Collaborative Edge-Edge Industrial Environments (CEIE) and precise multi-user QoS metrics. Thus, we focus on dynamic resource management within the CEIE architecture to better satisfy diverse industrial applications, formulating a multi-stage Mixed Integer Nonlinear Programming (MINLP) problem to minimize system costs. To reduce the computational complexity of solving the MINLP, we decompose the original problem into multi-user task offloading, Communication Resource Allocation (CmRA), and Computational Resource Allocation (CpRA) problems. These transformed problems are then tackled using DRMQ: an integrated learning optimization approach that combines model-free, priority experience replay-based Double Deep Q-Network (iDDQN) with model-based optimization, accelerating the Q-value function's convergence speed and reducing training time. Extensive simulations show that our proposed optimization scheme can reduce the average weighted system cost by at least 43.168% . Moreover, testbed experiments demonstrate that the proposed algorithm can reduce the average system cost by at least 42.650% in real-world applications, outperforming existing methods.

The proliferation of contaminated data on Internet of Things (IoT) devices has the potential to undermine the accuracy of data-driven decision-making by altering the distribution of original data. Existing data cleaning methods primarily depend on cloud center or cloud-edge cooperation, leading to prolonged data transmission delays and reduced cleaning accuracy. In this study, we identify edge server placement as a crucial step aligned with data cleaning and view the collaborative edge server placement with distributed data cleaning (SPDC) as a holistic problem. We comprehensively quantify the complexity of our issue through the analysis of numerous scenarios. To address this problem, we introduce a novel distributed collaborative edge framework comprising two key stages: server placement and data cleaning. We propose an optimized clustering algorithm for the former, considering the data distribution on the IoT layer and the constraints of the edge layer. For the latter, we introduce a gossip-based data cleaning algorithm that fully utilizes edge collaboration to enhance data cleaning accuracy. The algorithm exhibits an approximate performance complexity of O($\ln m$), where$m$represents the number of users’ tasks. Both theoretical analysis and experimental results reveal that our algorithm an average improvement in data cleaning accuracy of 9.02% and a reduction in delay of 36.61%, surpassing the performance of state-of-the-art works in various scenarios.

With the development of networks and smart devices, artificial intelligence has drawn more and more attention, especially in the Unmanned Aerial Vehicles. Therefore, it is quite critical to train and run DNNs on resource-limited and hardware-constrained UAVs. The traditional methods fail to adjust offloading strategy due to the dynamic environment, while recently proposed intelligent computation offloading techniques rely on accessing IoT devices’ private data, which leads to privacy and security problem. To alleviate the above problems, we propose an novel edge-intelligent-based computation offloading technology via Federated Learning (FL). Specially, we utilize Multi-Layer Perceptron (MLP) to learn the computation tasks features and offload different tasks to different smart devices. Besides, to protect data privacy and improve the system’s security, a hierarchical FL framework is utilized to train the model of the computation tasks features extraction. Finally, performance analysis results obtained by experiments demonstrate the performance of our proposed approach.

The growing popularity of contact-free smart sensing has contributed to the development of the Artificial Intelligence of Things (AIoT). The contact-free sensory data has great potential to mine and analyze the hidden information for AIoT-enabled applications. However, due to the limited storage resource of contact-free smart sensing devices, data is naturally stored in the cloud, which is at risk of privacy leakage. Cloud storage is generally considered insecure. On one hand, the openness of the cloud environment makes the data easy to be attacked, and the complex AIoT environment also makes the data transmission process vulnerable to the third party. On the other hand, the Cloud Service Provider (CSP) is untrusted. In this article, to ensure the security of data from contact-free smart sensing devices, a Cloud-Edge-End cooperative storage scheme is proposed, which takes full advantage of the differences in the cloud, edge, and end. Firstly, the processed sensory data is stored separately in the three layers by utilizing well-designed data partitioning strategy. This scheme can increase the difficulty of privacy leakage in the transmission process and avoid internal and external attacks. Besides, the contact-free sensory data is highly time-dependent. Therefore, combined with the Cloud-Edge-End cooperation model, this article proposes a delta-based data update method and extends it into a hybrid update mode to improve the synchronization efficiency. Theoretical analysis and experimental results show that the proposed cooperative storage method can resist various security threats in bad situations and outperform other update methods in synchronization efficiency, significantly reducing the synchronization overhead in AIoT.

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines.

In Edge Computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration; 2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively.

Localization for mobile group users is one of the important applications in cyber-physical systems. However, due to the sparse deployment of anchors and the instability of signals in the wireless environment, users cannot receive information from adequate anchors, which leads to the localization quality being undependable and unacceptable. To solve this problem, we propose exploiting the localized users as the mobile anchors for localizing the nonlocalized users. These mobile users cooperate as a whole group to improve the localization accuracy. Moreover, to decrease the communication cost among users, an algorithm for electing mobile anchors is designed, with several provable properties. This electing algorithm is a distributed method, without advanced negotiations among mobile users. In addition, for the scenarios with a crowd of users, we divide the users into different groups according to their distance information, which can ensure that only the dependable anchors are used for the localization. Extensive experimental results demonstrate that the localization dependability can be improved obviously. In terms of localization probability, our method outperforms the traditional fixed anchors-based method by approximately 70% with a small increment of communication cost (about 30%), and outperforms the method in which all the localized users are exploited as mobile anchors by about 50% with about a 70% decrement of communication cost.