In resource-constrained edge computing, the execution efficiency of workflow applications is significantly affected by bandwidth contention, especially during data transmissions between dependent tasks. However, existing workflow scheduling studies often struggle to optimize transmission delay effectively, whereas bandwidth slicing offers promising potential by leveraging the dynamic nature of bandwidth resources. To address this issue, we propose Workflow Scheduling with Dynamic Bandwidth Slicing (WSDBS), a novel scheduling algorithm that integrates bandwidth slicing into the workflow execution process. By introducing a dual-prediction strategy, WSDBS estimates the availability of both computational and bandwidth resources on servers, facilitating efficient task scheduling decisions under transmission uncertainty. Moreover, a novel transmission urgency metric is developed, which is derived from both link load and transmission criticality. This metric guides bandwidth slicing for the dynamic allocation of server-side bandwidth resources, ultimately alleviating contention among concurrent transmissions. Extensive experiments based on real-world Alibaba cluster traces show that WSDBS consistently improves scheduling efficiency, reducing the average makespan by 10.87%-14.44% over state-of-the-art baselines. These results validate its effectiveness in alleviating bandwidth contention and improving scheduling performance in edge computing environments.

To address the communication challenges associated with Federated Learning (FL), Decentralized Federated Learning (DFL) eliminates the central server and trains the model with decentralized method, enabling each client to only communicate with its neighbors. However, per our analysis, model trained with DFL experiences performance degradation because of data-heterogeneity and time-varying topologies. To address these issues, we propose a Dynamic K-step Gradient Tracking (DKGT) method to enhance the performance of DFL over time varying networks. Specifically, DKGT employs K-step local updates and gradient tracking to reduce the communication cost and the variance from heterogeneous data distribution, and we use dynamic gradient tracking parameter to correct gradient over time varying graph. Theoretically, we derive a universal convergence rate for smooth and non-convex problem at the rate of$\mathcal{O}\left(\frac{\left(f(\textbf{x}_0)-f(\textbf{x}^*)\right)}{\sqrt{T}(L\sqrt{KN})^{-1}-\tau(pKL\sqrt{TKN})^{-1}}+\frac{\sigma^2}{KTN\tau(pK-\tau)}\right)$, that τ and p respectively represent the time window length and the connectivity of time-varying networks. Experimentally, we illustrate the robustness and effectiveness of this heterogeneity correction on extensive non-convex neural network training tasks over different topologies and dynamic network settings.

In edge computing, workflow applications are optimally scheduled onto edge servers that are pre-equipped with the necessary services to satisfy stringent low-latency demands. However, prior research has not fully addressed the joint optimization of service placement and workflow scheduling, particularly the exploitation of task parallelism to reduce overall makespan. To address this shortcoming, we explore the combined workflow scheduling and service placement (WSP-SP) problem with the goal of minimizing the average makespan of applications. Recognizing that WSP-SP is NP-hard, we propose a two-stage, Parallelism Aware Workflow Scheduling and Service Placement strategy (PAWSSP) that minimizes the makespan with low complexity. In the first stage, a Parallelism Aware Service Placement module (PASP) is designed to adjust the service layout by allocating services with high parallelism onto distinct servers to fully leverage task-level concurrency. In the subsequent workflow scheduling stage, PAWSSP determines task priority by resolving inter-task competition and further reduces waiting times by assigning tasks to servers experiencing lower resource contention. We further extend PAWSSP to make it applicable to both offline and online scenarios. Extensive evaluations demonstrate that PAWSSP performs robustly across diverse scenarios, reducing the average makespan by 3.9%-14.7% compared to existing baselines, while maintaining modest runtime overhead.

As a privacy-preserving solution, federated learning (FL) demonstrates great potential in distributed model training, but limited bandwidth, particularly in near-field communication (NFC)-based systems, emerges as a key bottleneck by restricting the number of participating clients. To address this challenge, over-the-air FL leverages the superposition property of wireless multiple-access channels, enabling faster model training and accommodating more clients, even in bandwidth-constrained scenarios like NFC. However, due to its analog-integrated nature, the FL performance is also affected by other factors, such as channel noise. These motivate us to consider how the selected client set and channel noise affect FL performance. To explore this concern, in this article, we consider an over-the-air FL system with analog gradient aggregation and analyze the impact of the selected client set and channel noise on FL training performance. The theoretical analysis effectively shows the importance of the clients’ number and the power scaling factor to the FL training performance. Based on the theoretical analysis, we transform the global optimization problem into the client selection problem and propose a dynamic client selection scheme to optimize the training performance under the aggregation error constraint. Experimental results demonstrate that our proposed scheme can boost FL by speeding up the convergence of the global model (at least 35%) and saving energy consumption.

The fuzzy broad learning system (FBLS) has received increasing attention due to its ability to quickly train from broad learning systems (BLS) and interpretability with fuzzy inference. However, the randomness of BLS brings instability to the training performance of the model, so the hyperparameters of the model are crucial for its performance. Currently, many FBLS use grid search to determine hyperparameters. However, grid search brings longer search time and the parameters obtained have randomness, which may not necessarily be the optimal hyperparameters. In response to these challenges, this paper proposes a fuzzy broad learning system with automatic determination of hyperparameters (ADHFBLS). We construct a novel FBLS based on the interval type-2 fuzzy set and design an incremental learning algorithm for rules and enhancement nodes to support rapid model expansion. Meanwhile, a heuristic hyperparameter automatic optimization algorithm is designed to overcome the randomness and long optimization time of grid search. Experiments have shown that ADHFBLS has higher accuracy and shorter model tuning time compared to some state-of-the-art models based on FBLS.

In Mobile Edge Computing (MEC) scenarios, computational tasks are popularly deployed using containerization to isolate the runtime environment. To complete the execution of the task, the edge server first pulls the image, then instantiates and runs the container. Since it takes a lot of time for the edge server to download the image from the cloud, image reuse reduces the pulling latency significantly. However, the limited storage capacity of edge servers hinders image reuse. Recent works have enhanced reuse efficiency by leveraging the hierarchical structure of images and caching high-value layers. However, their efficiency remains limited due to the lack of multi-container collaboration. This paper proposes a novel container scheduling strategy based on image layer reuse and sequence arrangement (ILR-SA) for MEC scenarios, which achieves efficient scheduling by collaborating multiple containers. First, containers are greedily deployed into the edge cluster. Then, the execution sequence of containers is modeled as an optimal Hamiltonian path problem, efficiently solved by our proposed decomposition algorithm. Finally, an efficient image layer update strategy is used to achieve layer reuse. We conduct rigorous experiments to demonstrate that our proposed container scheduling strategy reduces the computational task completion time by up to 91.3% compared to existing approaches.

Federated learning (FL) is a distributed machine learning paradigm that enables heterogeneous devices to train a model collaboratively. Recognizing communication as a bottleneck in FL, existing communication-efficient solutions, e.g., HeteroFL and LotteryFL, etc., utilize gradient sparsification to reduce communication costs. However, existing solutions fail to address the dynamic bandwidth issue in which the bandwidth of each client is constantly changing throughout the training process. In this article, we propose AdaptiveFL, a communication-adaptive FL framework, considering the dynamic constraints of bandwidth. The design of AdaptiveFL follows two key steps: 1) in each round, each device selects a best-fit sub-model for communication per currently available bandwidth; and 2) to guarantee the performance of each sub-model sent under dynamic bandwidth constraints, AdaptiveFL employs a local training method that enables each device to train a "tailorable" local model, which can be tailored to any sparsity with competitive accuracy. We compare AdaptiveFL with several communication-efficient SOTA methods and demonstrate that AdaptiveFL outperforms other baselines by a large margin.

With the increasing integration of renewable energy sources into the power grid via power electronic devices, the dynamics of the system became more complex, leading to a broadened frequency spectrum of the electrical signals. Therefore, wideband synchronous measurement has become an essential tool for panoramic information perception of the grid. Current protocols are designed for fundamental frequency data transmit, which is ineffective for the wideband frequency data. In this article, we propose a wideband communication protocol that encompasses a message frame construction and a real-time communication technique for variable-length wideband data. The communication procedure of the message frames remains fully compatible with the IEEE C37.118.2 and can simultaneously transmit harmonic, interharmonic, and fundamental frequency measurement data. The protocol is instantiated on hardware and a wideband monitoring system to validate its effectiveness, followed by the theoretical and practical analysis of communication performance. The field application of the wideband measurement for an electric vehicle charging station is demonstrated to validate the efficiency and practicability of the proposed communication protocol.

This paper investigates the implementation of Federated Learning (FL) in an over-the-air computation system with volatile clients, where each client operates under a limited energy budget and may unexpectedly drop out during local training sessions. The dropout of clients not only wastes energy but also diminishes their participation frequency, necessitating careful client selection by the server in each communication round. However, the diversity of training tasks and the random nature of client dropout present challenges such as the absence of an explicit objective function and the unavailability of client performance metrics. To address these challenges, we first analyze the convergence of the over-the-air federated learning system with volatile clients to identify the key factor influencing the model's convergence speed. Building upon this analysis, we propose an approximation of the objective function as the optimization goal for client selection. To mitigate energy waste, we introduce a dynamic client selection strategy termed DCSE, based on Exp3 with multiple plays and energy constraints, aiming to reconcile the dilemma of unknown local training states and limited resource constraints. Theoretical analysis demonstrates that our proposed solution maintains a constant bound on the difference from the optimal solution, affirming its theoretical feasibility. Furthermore, experimental results validate the effectiveness of the proposed strategy in enhancing FL by accelerating convergence speed, improving test accuracy, and reducing wasted energy.

Today's Industrial Internet of Things (IIoT) have achieved excellent manufacturing efficiency and automation results by leveraging machine learning (ML) and deep learning (DL). However, trustworthiness of ML/DL brings significant challenges to IIoT. This article proposes an evolving deep multiple kernel learning network through genetic algorithm (KNGA). Our KNGA method uses genetic algorithm (GA) to find the best deep multiple kernel learning structure, including the weights and the topology of the model. Compared with the current well-known models, KNGA has advantages in three aspects: 1) It can achieve good results without using many samples during model training; 2) the model can evolve in the process of training, including self-growth, and self-pruning; and 3) its trustworthiness and reliability can be guaranteed. Moreover, the whole model ensures excellent performance and requires manual adjustment of only a few parameters. Extensive experiments on the UCI, KEEL, Caltech256, and MNIST datasets demonstrate the effectiveness and trustworthiness of the proposed method.

To overcome the challenge of limited bandwidth, client selection has been considered an effective method for optimizing Federated Learning (FL). However, since the volatility of the learning environment, the available clients exhibit some volatility over the training process in terms of client population, client data, training status, and transmitting status, which greatly increases the difficulty of client selection. To find a practical solution, we explore a client selection problem in volatile federated learning (Volatile FL). Specifically, we first derive the convergence analysis for non-convex and strongly convex cases to illustrate the main factors affecting the convergence speed. Then, we introduce the client utility to quantify the client’s contribution to model training and discuss the key problems of client selection in Volatile FL. For an efficient settlement, we propose CU-CS, a Combinatorial Multi-Arm Bandit (C2MAB) based decision scheme for the proposed selection problem. Theoretically, we prove that the regret of CU-CS is strictly bounded by a finite constant, justifying its theoretical feasibility. The experimental results demonstrate that our method significantly boosts FL by speeding up model convergence, promoting model accuracy, and reducing energy consumption.

Federated Learning (FL), as a privacy-preserving machine learning paradigm, has been thrusted into the limelight. As a result of the physical bandwidth constraint, only a small number of clients are selected for each round of FL training. However, existing client selection solutions (e.g., the vanilla random selection) typically ignore the heterogeneous data value of the clients. In this paper, we propose the contribution-based selection algorithm (Contribution-Based Exponential-weight algorithm for Exploration and Exploitation, CBE3), which dynamically updates the selection weights according to the impact of clients' data. As a novel component of CBE3, a scaling factor, which helps maintain a good balance between global model accuracy and convergence speed, is proposed to improve the algorithm's adaptability. Theoretically, we proved the regret bound of the proposed CBE3 algorithm, which demonstrates performance gaps between the CBE3 and the optimal choice. Empirically, extensive experiments conducted on Non-Independent Identically Distributed data demonstrate the superior performance of CBE3—with up to 10% accuracy improvement compared with K-Center and Greedy and up to 100% faster convergence compared with the Random algorithm.

Federated learning (FL) has shown great potential as a privacy-preserving solution to training a centralized model based on local data from available clients. However, we argue that, over the course of training, the available clients may exhibit some volatility in terms of the client population, client data, and training status. Considering these volatilities, we propose a new learning scenario termed volatile federated learning (volatile FL) featuring set volatility, statistical volatility, and training volatility. The volatile client set along with the dynamic of clients’ data and the unreliable nature of clients (e.g., unintentional shutdown and network instability) greatly increase the difficulty of client selection. In this article, we formulate and decompose the global problem into two subproblems based on alternating minimization. For an efficient settlement for the proposed selection problem, we quantify the impact of clients’ data and resource heterogeneity for volatile FL and introduce the cumulative effective participation data (CEPD) as an optimization objective. Based on this, we propose upper confidence bound-based greedy selection, dubbed UCB-GS, to address the client selection problem in volatile FL. Theoretically, we prove that the regret of UCB-GS is strictly bounded by a finite constant, justifying its theoretical feasibility. Furthermore, experimental results show that our method significantly reduces the number of training rounds (by up to 62%) while increasing the global model’s accuracy by 7.51%.

In addition to the stationary mobile edge computing (MEC) servers, a few MEC surrogates that possess a certain mobility and computation capacity, e.g., flying unmanned aerial vehicles (UAVs) and private vehicles, have risen as powerful counterparts for service provision. In this article, we design a two-stage online scheduling scheme, targeting computation offloading in a UAV-assisted MEC system. On our stage-one formulation, an online scheduling framework is proposed for dynamic adjustment of mobile users' CPU frequency and their transmission power, aiming at producing a socially beneficial solution to users. But the major impediment during our investigation lies in that users might not unconditionally follow the scheduling decision released by servers as a result of their individual rationality. In this regard, we formulate each step of online scheduling on stage one into a non-cooperative game with potential competition over the limited radio resource. As a solution, a centralized online scheduling algorithm, called ONCCO, is proposed, which significantly promotes social benefit on the basis of the users' individual rationality. On our stage-two formulation, we are working towards the optimization of UAV computation resource provision, aiming at minimizing the energy consumption of UAVs during such a process, and correspondingly, another algorithm, called WS-UAV, is given as a solution. Finally, extensive experiments via numerical simulation are conducted for an evaluation purpose, by which we show that our proposed algorithms achieve satisfying performance enhancement in terms of energy conservation and sustainable service provision.

The integrity of geomagnetic data is a critical factor in understanding the evolutionary process of Earth's magnetic field, as it provides useful information for near-surface exploration, unexploded explosive ordnance detection, and so on. Aimed to reconstruct undersampled geomagnetic data, this paper presents a geomagnetic data reconstruction approach based on machine learning techniques. The traditional linear interpolation approaches are prone to time inefficiency and high labor cost, while the proposed approach has a significant improvement. In this paper, three classic machine learning models, support vector machine, random forests, and gradient boosting were built. Besides, a deep learning algorithm, recurrent neural network, was explored to further improve the training performance. The proposed learning models were used to specify a continuous regression hyperplane from a training data. The specified regression hyperplane is a mapping of the relation between the mock-up missing data and the surrounding intact data. Afterward, the trained models, essentially the hyperplanes, were used to reconstruct the missing geomagnetic traces for validation, and they can be used for reconstructing further collected new field data. Finally, numerical experiments were derived. The results showed that the performance of our methods was more competitive in comparison with the traditional linear method, as the reconstruction accuracy was increased by approximately 10%~20%.

The wireless caching has attracted a lot of attention in recent years, since it can reduce the backhaul cost significantly and improve the user‐perceived experience. The existing works on the wireless caching and transmission mainly focus on the communication scenarios without eavesdroppers. When the eavesdroppers appear, it is of vital importance to investigate the physical‐layer security for the wireless caching aided networks. In this paper, a caching network is studied in the presence of multiple eavesdroppers, which can overhear the secure information transmission. We model the locations of eavesdroppers by a homogeneous Poisson Point Process (PPP), and the eavesdroppers jointly receive and decode contents through the maximum ratio combining (MRC) reception which yields the worst case of wiretap. Moreover, the main performance metric is measured by the average probability of successful transmission, which is the probability of finding and successfully transmitting all the requested files within a radius R. We study the system secure transmission performance by deriving a single integral result, which is significantly affected by the probability of caching each file. Therefore, we extend to build the optimization problem of the probability of caching each file, in order to optimize the system secure transmission performance. This optimization problem is nonconvex, and we turn to use the genetic algorithm (GA) to solve the problem. Finally, simulation and numerical results are provided to validate the proposed studies.