Federated Learning inherently faces the challenge of balancing privacy protection and classification accuracy due to the risks associated with parameter sharing and the limitations of model performance. This paper proposes Fed-WGCA, a novel federated learning framework that integrates Wasserstein GAN with Gradient Penalty (WGAN-GP) and Coordinate Attention mechanisms. WGAN-GP enhances data privacy by generating high-quality synthetic data, mitigates the impact of imbalanced samples, and significantly improves model stability and performance. The Coordinate Attention mechanism optimizes feature extraction by fusing spatial and channel information. Experimental results demonstrate that the proposed framework Fed-WGCA separately achieves accuracy improvements of 5.99% and 6.12% on the Fashion-MNIST and CIFAR-10 datasets compared with state-of-the-art best results while exhibiting enhanced robustness against membership inference, image reconstruction attacks and poisoning attack.

Mobile apps are ubiquitous, and have become an indispensable part of our daily life. It is crucial to ensure the correctness, security and performance of these apps through automated GUI modeling. UI Transition Graph (UTG) is an important way of app abstract and modeling. While there have been considerable research efforts on constructing UTG through static or dynamic analysis, obtaining a relatively accurate and complete UTG is challenging. To this end, we present an approach and tool RLDroid that synergistically combines static analysis, dynamic exploration and reinforcement learning techniques to construct UTGs for Android apps. Specifically, RLDroid first extracts a seed UTG through static analysis, and uses this UTG with a depth-first strategy to guide the dynamic exploration. Then, RLDroid provides a Q-learning-based strategy initialized with the generated partial UTG to enhance dynamic exploration and outputs the final UTG. Our experiments on 29 Android apps show that RLDroid identified a total of 871 nodes (i.e., UI pages) and 2726 edges (i.e., transitions) without any false positives, which significantly outperforms the state-of-the-art GUI modeling techniques. Our two exploration strategies, the seed-UTGguided exploration and the Q-learning-enhanced exploration, make positive contributions to improving the completeness of UTG. Furthermore, the UTGs generated by RLDroid are highly useful for automated GUI testing, resulting in a 60 % increase in code coverage and the discovery of 52 additional crashes.

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%.

Mobile edge computing is a promising computing paradigm to enhance Quality of Service (QoS) for end devices (EDs), because it sinks the resources of cloud server to the network edge, which significantly mitigates the serious transmission delay to facilitate service response efficiency, and thus directly improves the QoS. However, edge servers are reluctant to offer service without satisfactory compensation. Therefore, auction-based resource pricing schemes are widely investigated to incentivize servers to serve EDs. In this work, we propose an edge server placement scheme based on all-pay auction mechanism, which determines the optimal server-user ratio to maximize service provider (SP) profit. Experimental simulations verified that the profit of SP is maximized while the server-user ratio is approximately 25%, which also provides the theoretical basis for real-world deployment.

Abstract Flow admission control (FAC) aims to efficiently manage the service requests while maximizing the network utilization. With multiple connection requests, access delay or even service interruption may occur. This paper proposes a novel FAC approach to reduce the contention between the end nodes and ensure high utilization of the networking resources for software defined IIoT. First, incoming flows are classified into different priorities using back propagation neural network based on selected features representing the current network status. Second, with the designed flow admission policies, bandwidth and buffer size are estimated with stochastic network calculus model. Finally, the thresholds of the proposed FAC scheme are dynamically decided based on the above two parameters. Various flows are admitted or rejected via the proposed FAC to maintain real time processing. Unlike traditional FAC schemes rely on static priority systems, the proposed scheme leverages machine learning technique for dynamic flow prioritization and the stochastic network calculus model for precise estimation. Computer simulation reveals that the proposed scheme accurately classifies the flows, and substantially decreases the transmission delay and improves the network utilization compared to the existing FAC schemes. This highlights the superiority of the proposed scheme meeting the demands of software defined IIoT.

Software‐defined networking (SDN) decouples the control plane and data forwarding plane to overcome the limitations of traditional networking infrastructure. Among several communication protocols employed for SDN, OpenFlow is most widely used for the communication between the controller and switch. In this paper two packet scheduling schemes, FCFS‐Pushout (FCFS‐PO) and FCFS‐Pushout‐Priority (FCFS‐PO‐P), are proposed to effectively handle the overload issue of multiple‐switch SDN targeting the edge computing environment. Analytical models on their operations are developed, and extensive experiment based on a testbed is carried out to evaluate the schemes. They reveal that both of them are better than the typical FCFS‐Block (FCFS‐BL) scheduling algorithm in terms of packet wait time. Furthermore, FCFS‐PO‐P is found to be more effective than FCFS‐PO in the edge computing environment.