UAV Communications for Sustainable Federated Learning
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）
Quoc-Viet Pham, Ming Zeng, Rukhsana Ruby, Thien Huynh-The, Won-Joo Hwang
备注：
Accepted by IEEE Vehicular Technology correspondence 2021
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 variables. Hence, we leverage the decomposition technique and a successive convex approximation approach to develop an efficient algorithm, namely UAV for sustainable FL (UAV-SFL). Finally, simulations illustrate the potential of our proposed UAV-SFL approach in providing a sustainable solution for FL-based wireless networks, and in reducing the UAV transmit power by 32.95%, 63.18%, and 78.81% compared with the benchmarks.
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…
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-tim…
University of California, Riverside（加利福尼亚大学河滨分校）；The Ohio State University（俄亥俄州立大学）
Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems. In this paper, we introduce sustainable machine learning in federated learning settings, using rechargeable devices that can collect energy from the ambient environment. We propose a practical federated learning framework that leverages intermittent energy arrivals for training, with provable convergence guarantees. Our framework can be applied to a wide range of machine learning settings in networked environments, including distributed and…
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…
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…
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…
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…
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…
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…
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）
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…
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 …
Agentic Trust Coordination for Federated Learning through Adaptive Thresholding and Autonomous Decision Making in Sustainable and Resilient Industrial Networks
London South Bank University（伦敦南岸大学）；Instituto de Telecomunicações（电信研究所）
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）
Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting
DISMI, University of Modena and Reggio Emilia（DISMI，Modena和Reggio Emilia大学）；En\&Tech, University of Modena and Reggio Emilia（En\&Tech，Modena和Reggio Emilia大学）；DIN, University of Bologna（DIN，博洛尼亚大学）
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）
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）
Federated learning (FL) is an emerging technique used to train a machine-learning model collaboratively using the data and computation resource of the mobile devices without exposing privacy-sensitive user data. Appropriate incentive mechanisms that motivate the data and mobile-device owner to participate in FL is key to building a sustainable platform for FL. However, it is difficult to evaluate the contribution level of the devices/owners to determine appropriate rewards without large computation and communication overhead. This paper proposes a computation-and communication-efficient method…
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 …
Internet of Things (IoT) sensors in smart buildings are becoming increasingly ubiquitous, making buildings more livable, energy efficient, and sustainable. These devices sense the environment and generate multivariate temporal data of paramount importance for detecting anomalies and improving the prediction of energy usage in smart buildings. However, detecting these anomalies in centralized systems is often plagued by a huge delay in response time. To overcome this issue, we formulate the anomaly detection problem in a federated learning setting by leveraging the multi-task learning paradigm,…
Electrical vehicle (EV) raises to promote an eco-sustainable society. Nevertheless, the "range anxiety" of EV hinders its wider acceptance among customers. This paper proposes a novel solution to range anxiety based on a federated-learning model, which is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks. Specifically, the new approach extends the federated-learning structure with two components: anomaly detection and sharing policy. The first component identifies preventing factors in model learning, while the second component offers …
Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing fine-grained energy data and performing centralised learning may compromise users' privacy and leave them vulnerable to several attacks. This study…
Illinois Institute of Technology（伊利诺伊理工学院）；Argonne National Laboratory（阿贡国家实验室）
In this paper, we apply the Non-Orthogonal Multiple Access (NOMA) technique to improve the massive channel access of a wireless IoT network where solar-powered Unmanned Aerial Vehicles (UAVs) relay data from IoT devices to remote servers. Specifically, IoT devices contend for accessing the shared wireless channel using an adaptive $p$-persistent slotted Aloha protocol; and the solar-powered UAVs adopt Successive Interference Cancellation (SIC) to decode multiple received data from IoT devices to improve access efficiency. To enable an energy-sustainable capacity-optimal network, we study the j…
Rensselaer Polytechnic Institute（伦斯勒理工学院）；University of Moratuwa（莫拉图瓦大学）
We propose how a developing country like Sri Lanka can benefit from privacy-enabled machine learning techniques such as Federated Learning to detect road conditions using crowd-sourced data collection and proposed the idea of implementing a Digital Twin for the national road system in Sri Lanka. Developing countries such as Sri Lanka are far behind in implementing smart road systems and smart cities compared to the developed countries. The proposed work discussed in this paper matches the UN Sustainable Development Goal (SDG) 9: "Build Resilient Infrastructure, Promote Inclusive and Sustainabl…
Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system. Machine learning-based methods are widely used in CAVs for crucial tasks like perception, motion planning, and motion control, where machine learning models in CAVs are solely trained using the local vehicle data, and the performance is not certain when exposed to new environments or unseen conditions. Federated learning …
From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks
Hung Yen University of Technology and Education（兴安技术与教育大学）；University of Tasmania（塔斯马尼亚大学）；University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；Lac Hong University（乐鸿大学）；Manchester Metropolitan University（曼彻斯特城市大学）；International Hellenic University（国际希腊大学）
Indian Institute of Technology (BHU)（印度理工学院（贝拿勒斯印度教大学））；Missouri University of Science and Technology（密苏里科技大学）
With the enhancement of people's living standards and rapid growth of communication technologies, residential environments are becoming smart and well-connected, increasing overall energy consumption substantially. As household appliances are the primary energy consumers, their recognition becomes crucial to avoid unattended usage, thereby conserving energy and making smart environments more sustainable. An appliance recognition model is traditionally trained at a central server (service provider) by collecting electricity consumption data, recorded via smart plugs, from the clients (consumers…
Energy-saving deployment algorithms of UAV swarm for sustainable wireless coverage
College of Computer Science, South-Central University for Nationalities（中南民族大学计算机学院）；Engineering Systems and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学工程系统与设计支柱）
Exploring Federated Learning for Thermal Urban Feature Segmentation -- A Comparison of Centralized and Decentralized Approaches
Scientific Computing Center (SCC), Karlsruhe Institute of Technology (KIT)（科学计算中心（SCC），卡尔斯鲁厄理工学院（KIT））；Institute for Industrial Production (IIP), Karlsruhe Institute of Technology (KIT)（工业生产研究所（IIP），卡尔斯鲁厄理工学院（KIT））；Instituto de Física de Cantabria (IFCA), CSIC-UC（坎塔布里物理研究所（IFCA），CSIC-UC）
In this paper, we study how to optimize the federated edge learning (FEEL) in UAV-enabled Internet of things (IoT) for B5G/6G networks, from a deep reinforcement learning (DRL) approach. The federated learning is an effective framework to train a shared model between decentralized edge devices or servers without exchanging raw data, which can help protect data privacy. In UAV-enabled IoT networks, latency and energy consumption are two important metrics limiting the performance of FEEL. Although most of existing works have studied how to reduce the latency and improve the energy efficiency, fe…
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Northwestern University（西北大学）；Nanyang Technological University（南洋理工大学）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；Hunan University（湖南大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting
Democritus University of Thrace（德谟克利特色雷斯大学）；Imperial College London（帝国理工学院）；Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA)（加泰罗尼亚电信技术中心（CTTC/CERCA））；Nokia Bell Labs（诺基亚贝尔实验室）；IMT Atlantique（大西洋电信学校）；Inria（法国国家信息与自动化研究所）；LS2N（南特计算机科学与实验室（注：此处LS2N为实验室，按要求保留独立机构名））
