Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally. However, dynamic mobile networks with high mobility, intermittent connectivity, and bandwidth limitation severely hinder model updates to the cloud server. Although previous studies have typically addressed user mobility issue through task reassignment or predictive modeling, frequent migrations may result in high communication overhead. Addressing this challenge involves not only dealing with resource constraints, but also finding ways to mitigate the challenges posed by user migrations. We therefore propose a intertemporal incentive framework, FedCross, which ensures the continuity of FL tasks by migrating interrupted training tasks to feasible mobile devices. FedCross comprises two distinct stages: Specifically, in Stage 1, we address the task allocation problem across regions under resource constraints by employing a multi-objective migration algorithm to quantify the optimal task receivers. Moreover, we adopt evolutionary game theory to capture the dynamic decision-making of users, forecasting the evolution of user proportions across different regions to mitigate frequent migrations. In Stage 2, we utilize a procurement auction mechanism to allocate rewards among base stations, ensuring that those providing high-quality models receive optimal compensation. This approach incentivizes sustained user participation, thereby ensuring the overall feasibility of FedCross. Finally, experimental results validate the theoretical soundness of FedCross and demonstrate its significant reduction in communication overhead.

Due to the sensitivity of data, Federated Learning (FL) is employed to enable distributed machine learning while safeguarding data privacy and accommodating the requirements of various devices. However, in the context of semidecentralized FL, clients’ communication and training states are dynamic. This variability arises from local training fluctuations, heterogeneous data distributions, and intermittent client participation. Most existing studies primarily focus on stable client states, neglecting the dynamic challenges inherent in real-world scenarios. To tackle this issue, we propose a TRust-Aware clIent scheduLing mechanism called TRAIL, which assesses client states and contributions, enhancing model training efficiency through selective client participation. We focus on a semi-decentralized FL framework where edge servers and clients train a shared global model using unreliable intra-cluster model aggregation and inter-cluster model consensus. First, we propose an adaptive hidden semi-Markov model to estimate clients’ communication states and contributions. Next, we address a client-server association optimization problem to minimize global training loss. Using convergence analysis, we propose a greedy client scheduling algorithm. Finally, our experiments conducted on real-world datasets demonstrate that TRAIL outperforms state-of-the-art baselines, achieving an improvement of 8.7% in test accuracy and a reduction of 15.3% in training loss.

The exponential growth of the Internet of Things (IoT) necessitates the deployment of large-scale models capable of processing the complex and diverse data generated by IoT devices. However, the substantial memory requirements of these models pose significant challenges, especially in scenarios where rapid decision-making and low-latency responses are critical. To address these challenges, we propose three innovative strategies for optimizing large model usage in IoT environments. The first strategy is an adaptive loading scheme, which enables dynamic loading of individual model experts. The second strategy involves an expert-by-expert loading approach, further enhancing the ability to load experts as needed, which optimizes memory usage and accelerates computations. The third strategy employs an interlayer expert reuse mechanism, facilitating the efficient reuse of experts across different layers, thus enhancing response rates without compromising model accuracy. Importantly, these strategies can be directly applied to Mixture of Experts (MoE) large language models without requiring additional training, thereby providing a seamless and efficient solution for leveraging these models in memory-constrained, high-performance IoT environments.

Devices and servers in Federated Edge Learning (FEL) are self-interested and resource-constrained, making it critical to design incentives to improve model performance. However, dynamic network conditions raise energy consumption, while data heterogeneity undermines device cooperation. Current research overlooks the interplay between system efficiency and device clustering, resulting in suboptimal updates. To address these challenges, we develop BENCH, a bilateral pricing mechanism consisting of three core rules aimed at incentivizing participation from both devices and servers. Specifically, we first design a reward allocation rule, based on the Rubinstein bargaining model, which dynamically allocates rewards. Theoretically, we derive a closed-form solution for this rule, demonstrating BENCH achieves Nash equilibrium. Secondly, we design a device partitioning rule that leverages modularity to group similar devices, facilitating personalized edge aggregation to accelerate local data adaptation. Thirdly, we design an edge matching rule that employs the Kuhn-Munkres algorithm to balance the load at edge servers, thus minimizing the congestion. Together, these three rules enable hierarchical optimization of pricing and associations, effectively mitigating the impact of dynamic costs and device heterogeneity. Extensive experiments demonstrate BENCH's effectiveness in increasing device participation by 28.81% and improving model performance by 2.66% compared to state-of-the-art baselines.

With the rapid advancements in network technology and automation processes, the threats posed by cyberattacks have become increasingly significant. To address these threats, numerous researchers have developed various network intrusion detection systems (NIDS) to monitor network traffic. However, with the continuous complexification of 5G networks and the exponential increase in network traffic, the emergence of new attacks alongside the lack of interpretability in NIDS posed challenges to the performance and efficiency of network intrusion detection. To tackle these issues, this paper proposes a hybrid explainable network intrusion detection framework that combines the strengths of supervised and unsupervised learning, enabling effective detection of emerging attacks within the network. Specifically, we utilize a Light Gradient Boosting Machine (LightGBM) model for supervised learning, followed by the SHapley Additive exPlanations (SHAP) method for model explanation and feature selection. Additionally, we employ a network utilizing Convolutional Neural Network and Long short term memory for encoding and decoding (ACLNet) purposes in unsupervised learning. Finally, the results from these two learning processes are integrated for anomaly detection. The simulation experimental results on the NSL-KDD dataset verify that our approach generates detection performance on par with state-of-the-art methods while offering significantly enhanced interpretability and improving detection efficiency.

Logistic regression is an algorithm widely used for binary classification in various real-world applications such as fraud detection, medical diagnosis, and recommendation systems. However, training a logistic regression model with data from different parties raises privacy concerns. Secure Multi-Party Computation (MPC) is a cryptographic tool that allows multiple parties to train a logistic regression model jointly without compromising privacy. The efficiency of the online training phase becomes crucial when dealing with large-scale data in practice. In this paper, we propose an online efficient protocol for privacy-preserving logistic regression based on Function Secret Sharing (FSS). Our protocols are designed in the two non-colluding servers setting and assume the existence of a third-party dealer who only poses correlated randomness to the computing parties. During the online phase, two servers jointly train a logistic regression model on their private data by utilizing pre-generated correlated randomness. Furthermore, we propose accurate and MPC-friendly alternatives to the sigmoid function and encapsulate the logistic regression training process into a function secret sharing gate. The online communication overhead significantly decreases compared with the traditional secure logistic regression training based on secret sharing. We provide both theoretical and experimental analyses to demonstrate the efficiency and effectiveness of our method.

Point clouds, characterized by irregularity and disorder, are widely utilized in the domain of the Internet of Things, including applications such as terrain exploration and autonomous driving. 3D action recognition and semantic segmentation widely employ them. However, point cloud videos often exhibit massive data volume and contain substantial data redundancy. This situation is highly detrimental to the real-time applications of point clouds such as autonomous driving and virtual reality. Network pruning is imperative to mitigate the redundancy. This paper proposes a spatial and temporal dual-scale adaptive pruning (STDAP) method for point cloud videos based on the attention mechanism to reduce redundancy. This method operates in both temporal and spatial dimensions, enabling adaptive pruning of point cloud videos and cutting down the inference time and memory usage. Experimental results demonstrate that the proposed method outperforms state-of-the-art techniques regarding accuracy and inference time. It achieves notable acceleration while maintaining efficacy.

To solve the problem of low delivery rate, high network load and high average forwarding delay caused by blind forwarding of messages in mobile social networks considering the selfish attribute of nodes, the optimistic path analysis (OPA) algorithm is proposed. OPA puts forward the concept of encounter intensity based on the historical encounter records and gives its calculation formula. Encounter intensity uses time as an important basis, which can more accurately reflect the possibility of two nodes meeting next time. At the same time, based on the intensity of encounter, the “small world principle” is used to limit the number of hops of routing paths, and finally a set of preferred paths for the message from the source node to the destination node is constructed, which is used as a basis to determine whether the message is forwarded or not. Simulation results show that compared with classic routing algorithms, OPA can effectively increase message delivery rate, reduce network load and forwarding delay.

High reliability and low latency are conflicting when tasks are scheduled. Scheduling of parallel applications with data dependencies in heterogeneous systems is an NP-complete problem. Using replication to improve system reliability can lead to increased application execution time. From the perspective of increasing the reliability of real-time heterogeneous system considering communication overhead and the timing requirements, this paper proposed a fault-tolerant scheduling algorithm based on local maximum reliability replication strategy (FTSA-BLMR). Our algorithm first sets the maximum number of replications for each task. Then it continuously replicates the task with the highest system reliability for the current task set to obtain a new task set. The tasks in the new task set will be scheduled and the scheduling results will be recorded. Finally, the scheduling result will be selected as the final scheduling sequence, which has maximum system reliability and meets the deadline. The experimental results indicate that our algorithm can improve the system reliability by 40% compared with DB-FTSA when the deadline constraint is strict.

Abstract Nowadays, a new paradigm named mobile edge computing (MEC) is capable of supplying some cloud‐like functions at the edges of wireless networks, which enables vehicles to offload the computation intensive tasks on MEC servers with low latency. However, new challenges posed by the complex network environment and the mobility of vehicles are usually not covered by traditional offloading schemes. To solve such problems, we propose a heuristic task migration computation offloading (TMCO) scheme. Compared with traditional ones, TMCO can dynamically choose suitable places to offload the tasks for moving vehicles within deadline. For this purpose, the mobility of vehicle and strict delay deadline are considered comprehensively. We use hash table to store the number of tasks on the corresponding server and use random function to simulate the probability of task offloading. In terms of latency, experimental results suggest that the performance of TMCO is on average 10% higher than that of traditional full offloading schemes.

In a heterogeneous multi-core real-time system, once a permanent error occurs, the task cannot be successfully completed before the deadline, which may cause catastrophic consequences. Therefore, the reliability of the real-time system is critical. In this paper, we consider real-time tasks in a heterogeneous system with previous constraints, and explore how to improve the reliability of the system. We propose a new scheduling algorithm-PFTSA, which uses active replication to back up as many tasks on different processors as possible before the deadline, minimizing communication overhead, ensuring that the maximum number of permanent errors can be accommodated before the deadline and providing maximum system reliability. The experimental results show that the reliability of our proposed scheduling algorithm is higher than the existing related algorithms.

Due to the increasing demand for computing power in many industries, heterogeneous multi-core processors are needed to solve the problem. In order to make full use of multi-core computing resources, an effective scheduling strategy for heterogeneous multi-core processor tasks is required. Directed acyclic graph (DAG) is usually used to represent data dependencies between tasks. Each task needs to be executed in the order of its data dependencies. Research under this model has made great progress. In this article, we study and improve the DAG-based task model, taking into account the fact that not only one-way data transmission is possible between tasks, but also two-way data exchange. Based on this model, we propose two scheduling strategies, overall cutting scheduling (OCS) and greedy selection scheduling (GSS). As far as we know, there is currently no work considering the existence of a special task model of two-way transmission between tasks, nor has it considered task scheduling in two-way transmission. In order to evaluate and demonstrate its feasibility and practicability, we proposed a reference method and supplemented with large-scale system experiments. These experiments show that the scheduling efficiency of the proposed method is greatly improved.

With the gradual complexity and high parallelization of computing tasks, people have higher and higher requirements for the reliability and energy-saving performance of embedded systems. In this paper, we focus on the scheduling of parallel application in heterogeneous systems. Based on task replication and DVFS techniques, we propose two algorithms ERO and ORO that could reduce energy consumption while meeting the task reliability requirements. Comparative experiments with EFSRG and HRRM shown that ERO and ORO algorithms provide more energy savings and lower schedule lengths for the given reliability requirement.

For a set of periodic real-time tasks running on a multi-processor system, some tasks need access to shared resources, while the remaining tasks do not. This article aims to solve the problem of priority inversion caused by simultaneous access to shared resources by tasks in a multi-processor real-time system. We propose a task allocation model and partition scheduling algorithm based on the MSRP protocol, which is called SASR-MSRP. Firstly, the algorithm divides the task set into two categories based on whether the task accesses shared resources or not. Secondly, calculate the system utilization rate U of the task that accesses the shared resource and determine the execution priority of the task according to its non-increasing order and assign it to the corresponding processor. Finally, we use the EDF scheduling algorithm to sequentially allocate the remaining independent tasks to the idle time period of the application processor. This algorithm not only reduces the problem of priority inversion, but also improves the overall scheduling efficiency of the system.

Cost savings are very critical in modern heterogeneous computing systems, especially in embedded systems. Task scheduling plays an important role in cost savings. In this article, we tackle the problem of scheduling tasks on heterogeneous multicore embedded systems with the constraints of time and resources for minimizing the total cost, while considering the communication overhead. This problem is NP-hard and we propose several heuristic techniques— ISGG , RLD , and RLDG —to address the problem. Experimental results show that the proposed algorithms significantly outperform the existing approaches in terms of cost savings.

Summary Heterogeneous CPU‐GPU system is a powerful way to accelerate compute‐intensive applications, such as the subset‐sum problem. Many parallel algorithms for solving the problem have been implemented on graphics processing units (GPUs). However, these GPU implementations may fail to fully utilize all the CPU cores and the GPU resources. When the GPU performs computational task, only one CPU core is used to control the GPUs, and all the remaining CPU cores are in idle state, which leads to large amounts of available CPU resources being wasted. This paper proposes an efficient CPU‐GPU cooperative computing scheme for solving the subset‐sum problem, which enables the full utilization of all the computing power of both CPUs and GPUs. In order to find the most appropriate task distribution ratio between CPUs and GPUs, this paper establishes a simple but effective task distribution model. Considering the high CPU‐GPU communication overhead and the unbalanced workload between CPUs and GPUs may greatly reduce the performance, an incremental data transfer method is proposed to reduce the CPU‐GPU communication overhead, and a feedback‐based dynamic task distribution scheme is designed to effectively balance the workload between CPUs and GPUs during runtime. The experimental results show that the CPU‐GPU cooperative computing achieves a significant performance benefit over the CPU‐only or GPU‐only computing. Copyright © 2015 John Wiley & Sons, Ltd.

SUMMARY The subset‐sum problem is a well‐known non‐deterministic polynomial‐time complete (NP‐complete) decision problem. This paper proposes a novel and efficient implementation of a parallel two‐list algorithm for solving the problem on a graphics processing unit (GPU) using Compute Unified Device Architecture (CUDA). The algorithm is composed of a generation stage, a pruning stage, and a search stage. It is not easy to effectively implement the three stages of the algorithm on a GPU. Ways to achieve better performance, reasonable task distribution between CPU and GPU, effective GPU memory management, and CPU–GPU communication cost minimization are discussed. The generation stage of the algorithm adopts a typical recursive divide‐and‐conquer strategy. Because recursion cannot be well supported by current GPUs with compute capability less than 3.5, a new vector‐based iterative implementation mechanism is designed to replace the explicit recursion. Furthermore, to optimize the performance of the GPU implementation, this paper improves the three stages of the algorithm. The experimental results show that the GPU implementation has much better performance than the CPU implementation and can achieve high speedup on different GPU cards. The experimental results also illustrate that the improved algorithm can bring significant performance benefits for the GPU implementation. Copyright © 2014 John Wiley & Sons, Ltd.