Interest continues to grow in utilizing federated learning (FL) for various signal processing and communications applications. Over-the-air (OTA) computation has been proposed to improve FL efficiency in bandwidth-limited environments by leveraging the superposition characteristic of a wireless multiple-access channel (MAC). However, OTA FL faces inherent challenges due to channel noise and fading in any wireless MAC scenario, which can degrade optimization and significantly reduce model accuracy. This paper aims to design a robust OTA FL system to counteract the effects of noise and fading over time-varying channels. We propose a novel approach employing a Kalman filter (KF)-based OTA FL algorithm under imperfect channel state information (CSI). We conduct a convergence analysis of our OTA FL scheme, which motivates our development of a complementary hierarchical optimization methodology to minimize the impact of bias and noise terms. Numerical results confirm that our methodology has superior performance to conventional OTA FL, and approaches the performance obtained by the upper limit of perfect CSI in low-SNR scenarios.

This article investigates the security vulnerabilities of adversarial example-based image encryption by executing data reconstruction (DR) attacks on encrypted images. A representative image encryption method is the adversarial visual information hiding (AVIH), which uses type-I adversarial example training to protect gallery datasets used in image recognition tasks. In the AVIH method, the type-I adversarial example approach creates images that appear completely different but are still recognized by machines as the original ones. Additionally, the AVIH method can restore encrypted images to their original forms using a predefined private key generative model. For the best security, assigning a unique key to each image is recommended; however, storage limitations may necessitate some images sharing the same key model. This raises a crucial security question for AVIH: How many images can safely share the same key model without being compromised by a DR attack? To address this question, we introduce a dual-strategy DR attack against the AVIH encryption method by incorporating 1) generative-adversarial loss and 2) augmented identity loss, which prevent DR from overfitting-an issue akin to that in machine learning. Our numerical results validate this approach through image recognition and re-identification benchmarks, demonstrating that our strategy can significantly enhance the quality of reconstructed images, thereby requiring fewer key-sharing encrypted images. The source code to reproduce the results will be available in https://github.com/jonggyujang0123/Hiding_person.

We address a joint trajectory planning, user association, resource allocation, and power control problem to maximize proportional fairness in the aerial IoT network, considering practical end-to-end quality-of-service (QoS) and communication schedules. Though the problem is rather ancient, apart from the fact that the previous approaches have never considered user- and time-specific QoS, we point out a prevalent mistake in coordinate optimization approaches adopted by the majority of the literature. Coordinate optimization approaches, which repetitively optimize radio resources for a fixed trajectory and vice versa, generally converge to local optima when all variables are differentiable. However, these methods often stagnate at a non-stationary point, significantly degrading the network utility in mixed-integer problems such as joint trajectory and radio resource optimization. We detour this problem by converting the formulated problem into the Markov decision process (MDP). Exploiting the beneficial characteristics of the MDP, we design a non-iterative framework that cooperatively optimizes trajectory and radio resources without initial trajectory choice. The proposed framework can incorporate various trajectory-planning algorithms such as the genetic algorithm, tree search, and reinforcement learning. Extensive comparisons with diverse baselines verify that the proposed framework significantly outperforms the state-of-the-art method, nearly achieving the global optimum. Our implementation code is available athttps://github.com/hslyu/dbspf.

Recent research has shown a growing interest in per-instance differential privacy (pDP), highlighting the fact that each data instance within a dataset may incur distinct levels of privacy loss. However, conventional additive noise mechanisms apply identical noise to all query outputs, thereby deteriorating data statistics. In this study, we propose an instance-wise Laplace mechanism, which adds non-identical Laplace noises to the query output for each data instance. A challenge arises from the complex interaction of additive noise, where the noise introduced to individual instances impacts the pDP of other instances, adding complexity and resilience to straightforward solutions. To tackle this problem, we introduce an instance-wise Laplace mechanism algorithm via deep reinforcement learning and validate its ability to better preserve data statistics on a real dataset, compared to the original Laplace mechanism.

Deep neural networks (DNNs) have a risk of remembering sensitive data from their training datasets, inadvertently leading to substantial information leakage through privacy attacks like membership inference attacks. DP-SGD is a simple but effective defense method, incorporating Gaussian noise into gradient updates to safeguard sensitive information. With the prevalence of large neural networks, DP-signSGD, a variant of DP-SGD, has emerged, aiming to curtail memory usage while maintaining security. However, it is noteworthy that most DP-signSGD algorithms default to Gaussian noise, suitable only for DP-SGD, without scant discussion of its appropriateness for signSGD. Our study delves into an intriguing question: "Can we find a more efficient substitute for Gaussian noise to secure privacy in DP-signSGD?" We propose an answer with a Logistic mechanism, which conforms to signSGD principles and is interestingly evolved from an exponential mechanism. In this paper, we provide both theoretical and experimental evidence showing that our method surpasses DP-signSGD.

The growth in artificial intelligence (AI) technology has attracted substantial interests in latency-aware task offloading of mobile edge computing (MEC)—namely, minimizing service latency. Additionally, the use of MEC systems poses an additional problem arising from limited battery resources of MDs. This paper tackles the pressing challenge of latency-aware distributed task offloading optimization, where user association (UA), resource allocation (RA), full-task offloading, and battery of mobile devices (MDs) are jointly considered. In existing studies, joint optimization of overall task offloading and UA is seldom considered due to the complexity of combinatorial optimization problems, and in cases where it is considered, linear objective functions such as power consumption are adopted. Revolutionizing the realm of MEC, our objective includes all major components contributing to users’ quality of experience, including latency and energy consumption. To achieve this, we first formulate an NP-hard combinatorial problem, where the objective function comprises three elements: communication latency, computation latency, and battery usage. We derive a closed-form RA solution of the problem; next, we provide a distributed pricing-based UA solution. We simulate the proposed algorithm for various resource-intensive tasks. Our numerical results show that the proposed method Pareto-dominates baseline methods. More specifically, the results demonstrate that the proposed method can outperform baseline methods by1.62 times shorter latencywith41.2% less energy consumption.

Object detection in aerial images is a growing area of research, with maritime object detection being a particularly important task for reliable surveillance, monitoring, and active rescuing. Notwithstanding astonishing advances of computer visiontechnologies, detecting ships and floating matters in these images are challenging due to factors such as object distance. What makes it worse is pervasive sea surface effects such as sunlight reflection, wind, and waves. Hyperspectral image (HSI) sensors, providing more than 100 channels in wavelengths of visible and near-infrared, can extract intrinsic information of materials from a few pixels of HSIs.The advent of HSI sensors motivates us to leverage HSIs to circumvent false positives due to the sea surface effects.Unfortunately, there are few public HSI datasets due to the high cost and labor involved in collecting them, hindering object detection research based on HSIs. We have collected and annotated a new dataset called ``Multi-Modal Ship and flOating matter Detection in Aerial Images (M$^{2}$SODAI),'', which includes synchronized image pairs of RGB and HSI data, along with bounding box labels for nearly 6,000 instances per category. We also propose a new multi-modal extension of the feature pyramid network called DoubleFPN.Extensive experiments on our benchmark demonstrate that fusion of RGB and HSI data can enhance mAP, especially in the presence of the sea surface effects.

The renewable energy source (RES)-powered small cell base station (SBS) is a promising technology for the next-generation networks because RESs provide sustainable energy without being depleted. In RES-assisted networks, joint optimization of user association (UA) and power control (PC) is required to enhance the sum-rate performance by reducing inter-cell interference between SBSs. This paper tackles the UA and PC optimization for the sum-rate maximization under quality-of-service (QoS) and backhaul constraints. We formulate the problem as mixed-integer non-linear programming, in which UA and PC variables of different time slots are tightly coupled due to the RES energy dynamics model. Hence, designing a dynamic policy-based UA and PC with consideration of the future environments such as the quantity of channel gain and energy harvesting remains a challenge. First, we propose a deep unsupervised learning (DUL)-based UA scheme for a fixed PC variable. To lower the computational complexity and accelerate the convergence of the proposed learning-based optimization, we relax the UA variable, which originally has an extremely high dimension, into a low-dimensional continuous variable inspired by the Lagrangian method. Next, we propose a deep reinforcement learning (DRL)-based PC scheme, in which the stringent QoS and backhaul constraints are considered penalty terms on the reward design. The proposed DRL-based PC scheme facilitates dynamic PC inferring the relationship between the future environment and current PC. Simulation results demonstrate that the proposed scheme enhances the sum-rate by 10%, accommodates 3.3 percent point (%p) more QoS-qualified users, and reduces the computation time by 20 times compared to the conventional optimization-based method.

Renewable energy source (RES)-powered base stations have received tremendous research interest in recent years because they can expand network coverage without building a power grid. This paper proposes a novel user association (UA), resource allocation (RA), and dynamic power control (PC) scheme to maximize the$\alpha $-fairness in RES-assisted small cell networks. The$\alpha $-fairness is a general notion that flexibly adjusts the balance between the throughput, proportional fairness, and max-min fairness according to$\alpha $. Nevertheless, none of the existing studies has proposed UA, RA, and PC to maximize the$\alpha $-fairness due to its NP-hardness. Furthermore, fixed-policy-based PC designs cannot consider time-varying environments (e.g., energy harvesting models and wireless channels) of the RES-assisted networks. We first provide a Lagrangian duality-based algorithm to solve the UA and RA problem for a fixed PC. Next, we propose a dynamic PC scheme based on deep reinforcement learning (DRL) that chooses the best PC considering the time-varying environments. However, because the UA and RA algorithm executed in each step of the dynamic PC requires a long computation time, we aim to accelerate the computation of the UA and RA with DRL. Inspired by the Lagrangian duality, we design a DRL-based UA and RA with a low-dimensional continuous variable by relaxing the UA variable, the cardinality of which increases exponentially with the number of base stations and users. The simulation results show that the proposed scheme achieves a 100 times shorter computation time than the optimization-based schemes by computing only two neural networks. In particular, although there have been numerous studies on the proportional fairness maximization, the proposed scheme outperforms the optimization-based schemes in the throughput, proportional fairness, and max-min fairness metrics.

By virtue of their ultra high resolution, scanning electron microscopes (SEMs) are essential to study topography, morphology, composition, and crystallography of materials, and thus are widely used for advanced researches in physics, chemistry, pharmacy, geology, etc. The major hindrance of using SEMs is that obtaining high quality images from SEMs requires a professional control of many control parameters. Therefore, it is not an easy task even for an experienced researcher to get high quality sample images without any help from SEM experts. In this paper, we propose and implement a deep learning-based autonomous SEM machine, which assesses image quality and controls parameters autonomously to get high quality sample images just as if human experts do. This world's first autonomous SEM machine may be the first step to bring SEMs, previously used only for advanced researches due to its difficulty in use, into much broader applications such as education, manufacture, and mechanical diagnosis, which are previously meant for optical microscopes.

The fundamental problem in multi-cell two-way relaying is low uplink achievable rate due to the fact that the uplink signal transmitted by a user is significantly contaminated by the downlink signal transmitted simultaneously by the base station (BS) in the neighboring cell, which has much greater power than the power of the user uplink signal. In this paper, a novel relay precoding optimization scheme is proposed in the two-cell two-way relay channel with only local channel state information in pursuit of reducing the inter-cell interference. The optimization problems for finding the relay precoding coefficients are decoupled at each relay, and can be solved using the parameters which are calculated offline according to the users' locations. Simulation results show that the proposed scheme outperforms the conventional two-way relaying and direct communication without relaying for cell-edge users, owing to reduced inter-cell interference.