The problem of training federated learning(FL) algorithms which is used to perform intrusion detection and protect data privacy in actual wireless networks is studied. Specifically, unmanned aerial vehicles(UAVs) can move at high speeds that results the doppler effect, which further deteriorates the communicational quality of the wireless network interactions with the base station. Since all training parameters are transmitted through the wireless network, the quality of training is affected by many factors such as packet errors and the number of wireless resources. The federated learning and wireless communication is formulated as an optimization problem, whose goal is to minimize the FL loss function that captures the performance of the algorithm. To solve the problems, we present the PVBO: transmit power, velocity and resource block allocation joint optimization algorithm. Based on the expected convergence rate of the FL algorithm, the optimal transmit power and velocity for each UAV are derived, under a given uplink resource block allocation scheme. Then, the uplink resource block allocation is optimized so as to minimize the FL loss function. The simulation results show that it can averagely improve the accuracy of intrusion detection by 4.53 % compared to the randomized policy.
Semantic communication, as a next-generation communication technology, has proven to be highly effective in different tasks. However, the inherently open nature of wireless channels makes semantic communication systems particularly vulnerable to security risks, with eavesdropping being a primary concern. Despite these risks, security issues in semantic communication have not received sufficient attention. To fill this gap, we propose a novel Proactive Security Mechanism (PSM) within the semantic communication network to prevent eavesdroppers from obtaining private data. The main idea of the PSM is to ensure that legitimate users can communicate normally while deceiving eavesdroppers without arousing suspicion. To achieve this, the PSM actively adds controlled perturbations into the transmitted semantic information. To ensure that legitimate receivers can accurately eliminate these perturbations and maintain communication quality, we introduce Communication Identification Codes (CICs) in the PSM, which are assigned by the edge servers of the network. Furthermore, we build a semantic communication system based on the generative model and implement the PSM to verify the effectiveness of the PSM. The simulation results demonstrate that the PSM effectively ensures the privacy and confidentiality of data, thereby safeguarding the security of semantic communication.
This study aims to identify the most efficient and cost-effective approach to addressing the computational challenges associated with EEG-based biometric systems that use machine learning models. Traditional brain signal biometric analysis methods often extract features from a single EEG channel or a randomly selected subset of channels from the same brain region. However, given that essential physiological information is functionally distributed across multiple brain regions, this study systematically explores various combinations of EEG channels both within and across different brain regions. The biometric distinctiveness of EEG signals is evaluated under different emotional simulation conditions. Addressing the channel selection problem is critical, as it can lead to improved identification of optimal EEG sensor locations. EEG data were collected using all 32 available channels and the experimental results revealed that an optimally selected subset of eight channels outperformed the complete set of 32 channels in classification performance.
Backlit photography encounters challenges such as poor contrast, reduced visibility, and high noise levels, which adversely impact both human perception and computer vision systems. Current deep learning-based enhancement methods typically rely on paired images for training, yet the difficulty in acquiring such pairs gives rise to authenticity issues and artifacts in the enhanced images. Moreover, defining a standard for the ideal enhanced image is problematic due to the inherently subjective nature of lighting conditions. Although previous methods have successfully addressed unsupervised backlit image enhancement, the training process remains complex, with significant potential for improvement in both methodology and performance. In this regard, we propose a novel unsupervised approach that leverages the visual-semantic priors of the CLIP model to guide the enhancement process. Specifically, we begin by fine-tuning the CLIP image encoder using unpaired image data, refining its visual priors for backlit image enhancement. Subsequently, we achieve effective unsupervised backlit image enhancement through the use of the specialized CLIP image encoder tailored for this task, coupled with the CLIP text encoder, which provides integrated visual and semantic supervision. Our approach outperforms existing methods on the BAID dataset across multiple quality assessment metrics, establishing a new state-of-the-art for unsupervised backlit image enhancement.
Reconfigurable Intelligent Surface (RIS) has emerged as a promising enabling technology for wireless communications, which significantly enhances system performance through real-time manipulation of electromagnetic wave reflection characteristics. In RIS-assisted communication systems, existing deep learning-based channel state information (CSI) feedback methods often suffer from excessive parameter requirements and high computational complexity. To address this challenge, this paper proposes LwCSI-Net, a lightweight autoencoder network specifically designed for RIS-assisted multiple-input single-output (MISO) systems, aiming to achieve efficient and low-complexity CSI feedback. The core contribution of this work lies in an innovative lightweight feedback architecture that deeply integrates multi-layer convolutional neural networks (CNNs) with attention mechanisms. Specifically, the network employs 1D convolutional operations with unidirectional kernel sliding, which effectively reduces trainable parameters while maintaining robust feature-extraction capabilities. Furthermore, by incorporating an efficient channel attention (ECA) mechanism, the model dynamically allocates weights to different feature channels, thereby enhancing the capture of critical features. This approach not only improves network representational efficiency but also reduces redundant computations, leading to optimized computational complexity. Additionally, the proposed cross-channel residual block (CRBlock) establishes inter-channel information-exchange paths, strengthening feature fusion and ensuring outstanding stability and robustness under high compression ratio (CR) conditions. Our experimental results show that for CRs of 16, 32, and 64, LwCSI-Net significantly improves CSI reconstruction performance while maintaining fewer parameters and lower computational complexity, achieving an average complexity reduction of 35.63% compared to state-of-the-art (SOTA) CSI feedback autoencoder architectures.
