Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL enabled UAV swarms. In summary, this survey provides a comprehensive survey of various DL applications for UAV swarms in extensive scenarios.

Partial differential equations (PDEs) are widely used for description of physical and engineering phenomena. Some key parameters involved in PDEs, which represents certain physical properties with important scientific interpretations, are difficult or even impossible to be measured directly. Estimation of these parameters from noisy and sparse experimental data of related physical quantities is an important task. Many methods for PDE parameter inference involve a large number of evaluations of numerical solution of PDE through algorithms such as finite element method, which can be time-consuming especially for nonlinear PDEs. In this paper, we propose a novel method for estimating unknown parameters in PDEs, called PDE-Informed Gaussian Process Inference (PIGPI). Through modeling the PDE solution as a Gaussian process (GP), we derive the manifold constraints induced by the (linear) PDE structure such that under the constraints, the GP satisfies the PDE. For nonlinear PDEs, we propose an augmentation method that transfers the nonlinear PDE into an equivalent PDE system linear in all derivatives that our PIGPI can handle. PIGPI can be applied to multi-dimensional PDE systems and PDE systems with unobserved components. The method completely bypasses the numerical solver for PDE, thus achieving drastic savings in computation time, especially for nonlinear PDEs. Moreover, the PIGPI method can give the uncertainty quantification for both the unknown parameters and the PDE solution. The proposed method is demonstrated by several application examples from different areas.

Technological advancements have normalized the usage of unmanned aerial vehicles (UAVs) in every sector, spanning from military to commercial but they also pose serious security concerns due to their enhanced functionalities and easy access to private and highly secured areas. Several instances related to UAVs have raised security concerns, leading to UAV detection research studies. Visual techniques are widely adopted for UAV detection, but they perform poorly at night, in complex backgrounds, and in adverse weather conditions. Therefore, a robust night vision-based drone detection system is required to that could efficiently tackle this problem. Infrared cameras are increasingly used for nighttime surveillance due to their wide applications in night vision equipment. This paper uses a deep learning-based TinyFeatureNet (TF-Net), which is an improved version of YOLOv5s, to accurately detect UAVs during the night using infrared (IR) images. In the proposed TF-Net, we introduce architectural changes in the neck and backbone of the YOLOv5s. We also simulated four different YOLOv5 models (s,m,n,l) and proposed TF-Net for a fair comparison. The results showed better performance for the proposed TF-Net in terms of precision, IoU, GFLOPS, model size, and FPS compared to the YOLOv5s. TF-Net yielded the best results with 95.7\% precision, 84\% mAp, and 44.8\% $IoU$.

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. To ease communication burden, over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication. Based on the proposed system, the system implementation over wireless network is introduced and we provide the problem formulation. In particular, we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over MIMO channels. Therefore, the precoding matrix at the transmitter and the combining matrix at the receiver of each MIMO link, as well as the channel matrix itself, can jointly serve as a fully connected layer of the NN. The generalization of the proposed scheme to the conventional NNs is also introduced. Finally, we extend the proposed scheme to the widely used convolutional neural networks and demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations. In such a split ML system, the precoding and combining matrices are regarded as trainable parameters, while MIMO channel matrix is regarded as unknown (implicit) parameters.

在这项工作中，我们考虑了具有多个基站和间隔干扰的无线系统中的联合学习模型。在学习阶段，我们应用了一个不同的私人方案，将信息从用户传输到其相应的基站。我们通过在其最佳差距上得出上限来显示学习过程的收敛行为。此外，我们定义了一个优化问题，以减少该上限和总隐私泄漏。为了找到此问题的本地最佳解决方案，我们首先提出了一种计划资源块和用户的算法。然后，我们扩展了该方案，以通过优化差异隐私人工噪声来减少总隐私泄漏。我们将这两个程序的解决方案应用于联合学习系统的参数。在这种情况下，我们假设每个用户都配备了分类器。此外，假定通信单元的资源块比用户数量少。仿真结果表明，与随机调度程序相比，我们提出的调度程序提高了预测的平均准确性。此外，其具有噪声优化器的扩展版本大大减少了隐私泄漏的量。

迄今为止，通信系统主要旨在可靠地交流位序列。这种方法提供了有效的工程设计，这些设计对消息的含义或消息交换所旨在实现的目标不可知。但是，下一代系统可以通过将消息语义和沟通目标折叠到其设计中来丰富。此外，可以使这些系统了解进行交流交流的环境，从而为新颖的设计见解提供途径。本教程总结了迄今为止的努力，从早期改编，语义意识和以任务为导向的通信开始，涵盖了基础，算法和潜在的实现。重点是利用信息理论提供基础的方法，以及学习在语义和任务感知通信中的重要作用。

空中接入网络已被识别为各种事物互联网（物联网）服务和应用程序的重要驾驶员。特别是，以无人机互联网为中心的空中计算网络基础设施已经掀起了自动图像识别的新革命。这种新兴技术依赖于共享地面真理标记的无人机（UAV）群之间的数据，以培训高质量的自动图像识别模型。但是，这种方法将带来数据隐私和数据可用性挑战。为了解决这些问题，我们首先向一个半监督的联邦学习（SSFL）框架提供隐私保留的UAV图像识别。具体而言，我们提出了模型参数混合策略，以改善两个现实场景下的FL和半监督学习方法的天真组合（标签 - 客户端和标签 - 服务器），其被称为联合混合（FEDMIX）。此外，在不同环境中使用不同的相机模块，在不同环境中使用不同的相机模块，在不同的相机模块，即统计异质性，存在显着差异。为了减轻统计异质性问题，我们提出了基于客户参与训练的频率的聚合规则，即FedFReq聚合规则，可以根据其频率调整相应的本地模型的权重。数值结果表明，我们提出的方法的性能明显优于当前基线的性能，并且对不同的非IID等级的客户数据具有强大。

在移动边缘网络上部署深神经网络（DNN）的主要挑战是如何分离DNN模型，以匹配网络架构以及所有节点的计算和通信容量。这基本上涉及两个高耦合程序：模型生成和模型分裂。在本文中，提出了一种联合模型分割和神经结构搜索（JMSNAS）框架以在移动边缘网络上自动生成和部署DNN模型。考虑到计算和通信资源约束，配制计算图形搜索问题以查找DNN模型的多分裂点，然后培训模型以满足一些精度要求。此外，通过正确设计目标函数来实现模型精度和完成延迟之间的权衡。实验结果证实了通过最先进的分机学习设计方法的提出框架的优越性。

随着数据爆炸的不断趋势，将数据服务器从数据服务器传递到最终用户的数据包导致移动网络的Fronthaul和Reachthaula业务增加压力。为缓解此问题，将流行内容更接近最终用户的缓存是一种减少网络拥塞和提高用户体验的有效方法。为了找到内容缓存的最佳位置，许多传统方法构造了各种混合整数线性编程（MILP）模型。然而，由于维度固有的诅咒，这种方法可能无法支持在线决策。本文提出了一种用于主动缓存的新框架。该框架通过将优化问题转换为灰度图像来利用数据驱动技术来合并基于模型的优化。对于并行培训和简单的设计目的，所提出的MILP模型首先被分解为多个子问题，然后，训练卷积神经网络（CNNS）以预测这些子问题的内容高速缓存位置。此外，由于MILP模型分解忽略子问题之间的内部效果，因此CNNS的输出具有不可行的解决方案的风险。因此，提供了两个算法：第一个使用来自CNN的预测作为减少决策变量的数量的额外约束;第二个采用CNNS的输出来加速本地搜索。数值结果表明，与MILP解决方案相比，所提出的方案可以减少71.6％的计算时间，只有0.8％的额外性能成本，这为实时提供了高质量的决策。