This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by considering the interactions among them to make a flock requires a huge inter-UAV communication which is impossible to implement in real-time applications. One method of control is to apply the mean-field game (MFG) framework which substantially reduces communications among the UAVs. However, to realize this framework, powerful processors are required to obtain the control laws at different UAVs. This requirement limits the usage of the MFG framework for real-time applications such as massive UAV control. Thus, a function approximator based on neural networks (NN) is utilized to approximate the solutions of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Nevertheless, using an approximate solution can violate the conditions for convergence of the MFG framework. Therefore, the federated learning (FL) approach which can share the model parameters of NNs at drones, is proposed with NN based MFG to satisfy the required conditions. The stability analysis of the NN based MFG approach is presented and the performance of the proposed FL-MFG is elaborated by the simulations.
Qatar University（卡塔尔大学）；Qualcomm Inc（高通公司）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
The usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches. Despite the abundance of such advantages, it is imperative to investigate the performance of UAV utilization while considering their design limitations. This paper investigates the deployment of UAV swarms when each UAV carries a machine learning classification task. To avoid data exchange with ground-based processing nodes, a federated learning approach is adopted between a UAV leader and the swarm members to impro…
Korean Southeast Center for the 4th Industrial Revolution Leader Education, Pusan National University（韩国釜山国立大学第四次工业革命领袖教育韩国东南中心）；Laval University（拉瓦尔大学）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；Kumoh National Institute of Technology（金乌工科大学）；Department of Biomedical Convergence Engineering, Pusan National University（釜山国立大学生物医学融合工程系）
Federated learning (FL), invented by Google in 2016, has become a hot research trend. However, enabling FL in wireless networks has to overcome the limited battery challenge of mobile users. In this regard, we propose to apply unmanned aerial vehicle (UAV)-empowered wireless power transfer to enable sustainable FL-based wireless networks. The objective is to maximize the UAV transmit power efficiency, via a joint optimization of transmission time and bandwidth allocation, power control, and the UAV placement. Directly solving the formulated problem is challenging, due to the coupling of variab…
To prolong the lifetime of the unmanned aerial vehicles (UAVs), the UAVs need to fulfill their missions in the shortest possible time. In addition to this requirement, in many applications, the UAVs require a reliable internet connection during their flights. In this paper, we minimize the travel time of the UAVs, ensuring that a probabilistic connectivity constraint is satisfied. To solve this problem, we need a global model of the outage probability in the environment. Since the UAVs have different missions and fly over different areas, their collected data carry local information on the net…
National Yang Ming Chiao Tung University（国立阳明交通大学）；Mississippi State University（密西西比州立大学）；Texas Tech University（德克萨斯理工大学）
Federated learning (FL) is a promising distributed learning technique particularly suitable for wireless learning scenarios since it can accomplish a learning task without raw data transportation so as to preserve data privacy and lower network resource consumption. However, current works on FL over wireless networks do not profoundly study the fundamental performance of FL over wireless networks that suffers from communication outage due to channel impairment and network interference. To accurately exploit the performance of FL over wireless networks, this paper proposes a novel intermittent …
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Shanghai Jiao Tong University（上海交通大学）；Nanjing University（南京大学）；The Hong Kong Polytechnic University（香港理工大学）
Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. Empowering UAVs networks intelligence by artificial intelligence (AI) especially machine learning (ML) techniques is inevitable and appealing to enable the aforementioned applications. To solve the problems of traditional cloud-centric ML for UAV networks such as privacy concern, unacceptable latency, and resource burden, a distributed ML technique, \textit(i.e.), federated learning (FL), has been recently proposed to enable mu…
Purdue University（普渡大学）；University at Buffalo (SUNY)（纽约州立大学布法罗分校）；Duke University（杜克大学）
We investigate training machine learning (ML) models across a set of geo-distributed, resource-constrained clusters of devices through unmanned aerial vehicles (UAV) swarms. The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource p…
With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace humans in jobs that they cannot perform. As a new type of IoT vehicle, the current status and trend of research on Unmanned Aerial Vehicle(UAV) is gratifying, and the development prospect is very promising. However, privacy and communication are still very serious issues in drone applications. This is because most drones still use centralized cloud-based da…
Heilongjiang University（黑龙江大学）；Nanyang Technological University（南洋理工大学）；Georgia Southern University（佐治亚南方大学）；Alibaba Group（阿里巴巴集团）；Alibaba-NTU Joint Research Institute（阿里巴巴-南洋理工大学联合研究院）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly（南洋理工大学-不列颠哥伦比亚大学积极乐龄卓越联合研究中心）
Due to air quality significantly affects human health, it is becoming increasingly important to accurately and timely predict the Air Quality Index (AQI). To this end, this paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting. Specifically, in the air, this framework leverages a light-weight Dense-MobileNet model to achieve energy-efficient end-to-end learning from haze features of haze images taken by Unmanned Aerial Vehicles (UAVs) for predicting AQI scale distribution. Furthermore, the Federated …
Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation
Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)（深圳未来智能网络研究院）；School of Science and Engineering (SSE)（科学与工程学院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Advanced Technology (SIAT)（深圳先进技术研究院）；Chinese Academy of Sciences (CAS)（中国科学院）；Southern University of Science and Technology（南方科技大学）；Imperial College London（伦敦帝国学院）；Central South University（中南大学）；Shenzhen University of Advanced Technology（深圳理工大学）
EURECOM（欧洲通信学院）；Mitsubishi Electric R&D Centre Europe（三菱电机欧洲研发中心）
In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communities exist, each defined by a unique task to be learned. In this setting, spatially distributed devices belonging to each community collaboratively contribute towards training their community model via wireless links provided by the UAV. Accordingly, the UAV acts as a mobile orchestrator coordinating the transmissions and the learning schedule among the devices in each community, intending to accelerate the learning pro…
Virginia Tech（弗吉尼亚理工大学）；University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；Ericsson Research（爱立信研究院）；Princeton University（普林斯顿大学）；University of Oulu（奥卢大学）
Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of continuous connections between the UAV swarm and ground base stations (BSs), using centralized ML will be challenging, particularly when dealing with a large volume of data. In this paper, a novel framework is proposed to implement distributed federated learning (FL) algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. Each following UAV trains a l…
Personalized Federated Learning by Energy-Efficient UAV Communications
Department of Computer Science, North Carolina State University（计算机科学系，北卡罗来纳州立大学）；Department of Electrical and Computer Engineering, University of Massachusetts（电气与计算机工程系，马萨诸塞大学）
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Queen’s University（女王大学）；University of Sharjah（沙迦大学）
Unmanned aerial vehicles (UAVs) mobility enables flexible and customized federated learning (FL) at the network edge. However, the underlying uncertainties in the aerial-terrestrial wireless channel may lead to a biased FL model. In particular, the distribution of the global model and the aggregation of the local updates within the FL learning rounds at the UAVs are governed by the reliability of the wireless channel. This creates an undesirable bias towards the training data of ground devices with better channel conditions, and vice versa. This paper characterizes the global bias problem of a…
ENS（巴黎高等师范学院）；CNRS（法国国家科学研究中心）；PSL Research University（巴黎文理研究大学）
To date, the most popular federated learning algorithms use coordinate-wise averaging of the model parameters. We depart from this approach by differentiating client contributions according to the performance of local learning and its evolution. The technique is inspired from control theory and its classification performance is evaluated extensively in IID framework and compared with FedAvg.
International Institute of Information Technology (IIIT), Hyderabad（国际信息技术学院（海得拉巴））
Deep learning's unprecedented success raises several ethical concerns ranging from biased predictions to data privacy. Researchers tackle these issues by introducing fairness metrics, or federated learning, or differential privacy. A first, this work presents an ethical federated learning model, incorporating all three measures simultaneously. Experiments on the Adult, Bank and Dutch datasets highlight the resulting ``empirical interplay" between accuracy, fairness, and privacy.
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains
Lab of Systems Engineering and Robotics (LASER), Department of Computer Systems, Universidade Federal da Paraiba（系统工程与机器人实验室（LASER），计算机系统系，巴伊帕巴联邦大学）；Department of Cybernetics, Czech Technical University in Prague（控制论系，布拉格捷克技术大学）
University of Waterloo（滑铁卢大学）；Amazon Advertising（亚马逊广告）；Huawei Noah Ark’s Lab（华为诺亚方舟实验室）
Federated learning has emerged as a promising, massively distributed way to train a joint deep model over large amounts of edge devices while keeping private user data strictly on device. In this work, motivated from ensuring fairness among users and robustness against malicious adversaries, we formulate federated learning as multi-objective optimization and propose a new algorithm FedMGDA+ that is guaranteed to converge to Pareto stationary solutions. FedMGDA+ is simple to implement, has fewer hyperparameters to tune, and refrains from sacrificing the performance of any participating user. We…
The performance of federated learning systems is bottlenecked by communication costs and training variance. The communication overhead problem is usually addressed by three communication-reduction techniques, namely, model compression, partial device participation, and periodic aggregation, at the cost of increased training variance. Different from traditional distributed learning systems, federated learning suffers from data heterogeneity (since the devices sample their data from possibly different distributions), which induces additional variance among devices during training. Various varian…
On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning
School of Electronics and Information, Northwestern Polytechnical University（电子工程学院，西北工业大学）；DecoreX Intelligent Technologies Co., Ltd.（DecoreX智能科技有限公司）；Samsung AI Center（三星人工智能中心）；th Research Institute of China North Industries Group Corporation（中国北方工业集团208研究所）；Department of Electrical and Computer Engineering, University of Houston（电子与计算机工程系，休斯顿大学）
When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence
School of Informatics, Xiamen University, China（厦门大学信息学院，中国）；Internet of Things Thrust, Hong Kong University of Science and Technology (Guangzhou), China（香港科技大学（广州）物联网研究组，中国）
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；College of Mathematics and Computer Science, Fuzhou University（福州大学数学与计算机科学学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
Mobile crowdsensing (MCS) is an emerging sensing data collection pattern with scalability, low deployment cost, and distributed characteristics. Traditional MCS systems suffer from privacy concerns and fair reward distribution. Moreover, existing privacy-preserving MCS solutions usually focus on the privacy protection of data collection rather than that of data processing. To tackle faced problems of MCS, in this paper, we integrate federated learning (FL) into MCS and propose a privacy-preserving MCS system, called \textsc{CrowdFL}. Specifically, in order to protect privacy, participants loca…
Heilongjiang University（黑龙江大学）；Dalian Minzu University（大连民族大学）；University College London（伦敦大学学院）；Singapore University of Technology and Design（新加坡科技设计大学）；Guangdong University of Technology（广东工业大学）；Nanyang Technological University（南洋理工大学）
Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a Semi-supervised …
Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learning models without sharing raw data. SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server. Moreover, the split model makes SL a better option for resource-constrained environments. However, SL performs slower than FL due to the relay-based training across multiple clients. In this regard, this paper presents a novel approach, named splitfed learni…
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
Department of Computer Science and Engineering, BITS Pilani Dubai Campus（计算机科学与工程系，比斯帕尼迪拜校区）；Department of Computer Science, Missouri University of Science and Technology（计算机科学系，密苏里科技大学）
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of New South Wales（新南威尔士大学）；Weizmann Institute of Science（魏茨曼科学研究所）；Princeton University（普林斯顿大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen Research Institute of Trustworthy Autonomous Systems（深圳市可信自主系统研究院）；University of Macau（澳门大学）
Realizing human-like perception is a challenge in open driving scenarios due to corner cases and visual occlusions. To gather knowledge of rare and occluded instances, federated learning assisted connected autonomous vehicle (FLCAV) has been proposed, which leverages vehicular networks to establish federated deep neural networks (DNNs) from distributed data captured by vehicles and road sensors. Without the need of data aggregation, FLCAV preserves privacy while reducing communication costs compared with conventional centralized learning. However, it is challenging to determine the network res…
LLMs meet Federated Learning for Scalable and Secure IoT Management
School of Computer Science and Technology, Algoma University（计算机科学与技术学院，阿尔戈马大学）；School of Electrical Engineering and Computer Science, University of Ottawa（电气工程与计算机科学学院，渥太华大学）
FedPoP: Federated Learning Meets Proof of Participation
IMDEA Networks Institute（IMDEA网络研究所）；Universidad Carlos III de Madrid（卡洛斯三世大学）；University of California, Irvine（加州大学尔湾分校）；Stealth Software Technologies Inc.（隐形软件技术公司）
Anomaly detection for the Internet of Things (IoT) is a major intelligent service required by many fields, including intrusion detection, device-activity analysis, and security supervision. However, the heterogeneous distribution of data and resource-constrained end nodes present challenges for existing anomaly detection models. Due to the advantages of flexible deployment and multi-dimensional resources, high altitude platform stations (HAPSs) and unmanned aerial vehicles (UAVs), which are important components of vertical heterogeneous networks (VHetNets), have significant potential for sensi…
Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning that can be implemented in the IoV. However, the performance of the FL suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitat…
Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Vehicles (IoV) components enable effective Artificial Intelligence (AI) based models to be built. Beyond ground data sources, Unmanned Aerial Vehicles (UAVs) based service providers for data collection and AI model training, i.e., Drones-as-a-Service, is increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impedes data sharing across independently owned UAVs. To this end, we propose the adoption of a Federated Learning (FL) based…
