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Federated Learning (FL) revolutionizes collaborative machine learning among Internet of Things (IoT) devices by enabling them to train models collectively while preserving data privacy. FL algorithms fall into two primary categories: synchronous and asynchronous. While synchronous FL efficiently handles straggler devices, its convergence speed and model accuracy can be compromised. In contrast, asynchronous FL allows all devices to participate but incurs high communication overhead and potential model staleness. To overcome these limitations, the paper introduces a semi-synchronous FL framework that uses client tiering based on computing and communication latencies. Clients in different tiers upload their local models at distinct frequencies, striking a balance between straggler mitigation and communication costs. Building on this, the paper proposes the Dynamic client clustering, bandwidth allocation, and local training for semi-synchronous Federated learning (DecantFed) algorithm to dynamically optimize client clustering, bandwidth allocation, and local training workloads in order to maximize data sample processing rates in FL. DecantFed dynamically optimizes client clustering, bandwidth allocation, and local training workloads for maximizing data processing rates in FL. It also adapts client learning rates according to their tiers, thus addressing the model staleness issue. Extensive simulations using benchmark datasets like MNIST and CIFAR-10, under both IID and non-IID scenarios, demonstrate DecantFed’s superior performance. It outperforms FedAvg and FedProx in convergence speed and delivers at least a 28% improvement in model accuracy, compared to FedProx. 

Electricity theft is a type of cyberattack posing significant risks to the security of smart grids. Semi-supervised outlier detection (SSOD) algorithms utilize normal power usage data to build detection models, enabling them to detect unknown electricity theft attacks. In this paper, we applied feature engineering and ensemble learning to improve the detection performance of SSOD algorithms. Specifically, we extracted 22 time-series and wavelet features from load profiles, which served as inputs for the seven popular SSOD algorithms investigated in this study. Experimental results demonstrate that the proposed feature engineering greatly enhances the performance of SSOD algorithms to detect various false data injection (FDI) attacks. Furthermore, we constructed bagged ensemble models using the best-performing SSOD algorithm as the base model, with results indicating further improvements in detection performance compared to the base model alone. 

The paper outlines the design, prototyping, and simulation processes involved in creating a compact radio frequency (RF) backscatter communication system, powered by Organic Photovoltaic (OPV) cells. This system is integral to a mine rescue operation, particularly useful in scenarios where miners are trapped due to accidents. In such situations, a rescue drone, equipped with a searchlight and the discussed communication system, takes the lead in the assisted escape mission for miners. The drone establishes duplex communication with the miners through a battery-free, wearable transponder device. Initial experiments employing a RF backscatter testbed - which utilizes both software-defined radios and OPV cells - were conducted. These preliminary tests were crucial for assessing the conditions necessary for successful backscatter communication, as well as for evaluating the energy-harvesting performance of the system. Findings from these experiments indicate that the device can operate battery-free, powered solely by OPV cells, even under low illuminance levels of less than 75 lux. In the pursuit of crafting the device in a compact form, a co-design initiative was launched. This effort focused on developing a meander dipole antenna in tandem with the OPV cells, targeting a resonant frequency of 912 MHz. Simulation results, obtained from ANSYS HFSS, revealed significant changes in antenna impedance and S parameters yet minimal impact on the radiation pattern of the antenna with the integration of the layered OPV structure. 

In this Letter, we propose and investigate a retroreflective optical integrated sensing and communication (RO-ISAC) system using orthogonal frequency division multiplexing (OFDM) and corner cube reflector (CCR). To accurately model the reflected sensing channel of the RO-ISAC system, both a point source model and an area source model are proposed according to the two main types of light sources that are widely used. Detailed theoretical and experimental results are presented to verify the accuracy of the proposed channel models and evaluate the communication and sensing performance of the considered RO-ISAC system. 

Modern advances in unmanned aerial vehicle (UAV) technology have widened the scope of commercial and military applications. However, the increased dependency on wireless communications exposes UAVs to potential attacks and introduces new threats, especially from UAVs designed with the malicious intent of targeting vital infrastructures. Significant efforts have been made from researchers and other United States (U.S.) Department of Defense (DoD) agencies for developing countermeasures for detection, interception, or destruction of the malicious UAVs. One promising countermeasure is the use of a counter UAV (CUAV) swarm to detect, track, and neutralize the malicious UAV. This paper aims to recognize the state-of-the-art swarm intelligence algorithms for achieving cooperative capture of a mobile target UAV. The major design and implementation challenges for swarm control, algorithm architecture, and safety protocols are considered. A prime challenge for UAV swarms is a robust communication infrastructure to enable accurate data transfer between UAVs for efficient path planning. A multi-agent deep reinforcement learning approach is applied to train a group of CUAVs to intercept a faster malicious UAV, while avoiding collisions among other CUAVs and non-cooperating obstacles (i.e. other aerial objects maneuvering in the area). The impact of the latency incurred through UAV-to-UAV communications is showcased and discussed with preliminary numerical results.