Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles
Virginia Tech（弗吉尼亚理工大学）；Princeton University（普林斯顿大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Future Network of Intelligence Institute（未来网络智能研究院）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Oulu（奥卢大学）
Tengchan Zeng, Omid Semiari, Mingzhe Chen, Walid Saad, Mehdi Bennis
A new federated learning (FL) framework enabled by large-scale wireless connectivity is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs). In this framework, the learning models used by the controllers are collaboratively trained among a group of CAVs. To capture the varying CAV participation in the FL training process and the diverse local data quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, as well as the unbalanced and nonindependent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal autonomous controller. In particular, the impacts of varying CAV participation in the FL process and diverse CAV data quality on the convergence of the proposed DFP algorithm are explicitly analyzed. Leveraging this analysis, an incentive mechanism based on contract theory is designed to improve the FL convergence speed. Simulation results using real vehicular data traces show that the proposed DFP-based controller can accurately track the target CAV speed over time and under different traffic scenarios. Moreover, the results show that the proposed DFP algorithm has a much faster convergence compared to popular FL algorithms such as federated averaging (FedAvg) and federated proximal (FedProx). The results also validate the feasibility of the contract-theoretic incentive mechanism and show that the proposed mechanism can improve the convergence speed of the DFP algorithm by 40% compared to the baselines.
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…
Universidade de Santiago de Compostela（圣地亚哥-德孔波斯特拉大学）；Universidade da Coruña（拉科鲁尼亚大学）
Federated Learning is a novel framework that allows multiple devices or institutions to train a machine learning model collaboratively while preserving their data private. This decentralized approach is prone to suffer the consequences of data statistical heterogeneity, both across the different entities and over time, which may lead to a lack of convergence. To avoid such issues, different methods have been proposed in the past few years. However, data may be heterogeneous in lots of different ways, and current proposals do not always determine the kind of heterogeneity they are considering. …
FedPIDAvg: A PID controller inspired aggregation method for Federated Learning
École Normale Supérieure, PSL University（巴黎高等师范学院，巴黎文理研究大学）；Technical University of Munich（慕尼黑工业大学）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学TranslaTUM转化癌症研究中央研究所）；Helmholtz Zentrum Muenchen（慕尼黑亥姆霍兹中心）
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
University of Applied Sciences（应用科学大学）；University of Skikda（斯基克达大学）；University of Dubai（迪拜大学）；University of Sharjah（沙迦大学）；De Montfort University（德蒙福特大学）；Qatar University（卡塔尔大学）；University of Klagenfurt（克拉根福大学）
Nowadays, AI companies improve service quality by aggressively collecting users' data generated by edge devices, which jeopardizes data privacy. To prevent this, Federated Learning is proposed as a private learning scheme, using which users can locally train the model without collecting users' raw data to servers. However, for machine-learning applications on edge devices that have hard memory constraints, implementing a large model using FL is infeasible. To meet the memory requirement, a recent collaborative learning scheme named split federal learning is a potential solution since it keeps …
Federated Learning for Data-Driven Feedforward Control: A Case Study on Vehicle Lateral Dynamics
Center for Vision, Automation & Control, AIT Austrian Institute of Technology GmbH（视觉、自动化与控制中心，奥地利技术研究院）；Robert Bosch GmbH（博世集团）；Automation and Control Institute, TU Wien（自动化与控制研究所，维也纳技术大学）
Fraunhofer Institute for Transportation and Infrastructure Systems IVI（弗劳恩霍夫交通与基础设施系统研究所IVI）；Technical University of Munich（慕尼黑工业大学）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）
Deep learning is a key approach for the environment perception function of Cooperative Intelligent Transportation Systems (C-ITS) with autonomous vehicles and smart traffic infrastructure. In today's C-ITS, smart traffic participants are capable of timely generating and transmitting a large amount of data. However, these data can not be used for model training directly due to privacy constraints. In this paper, we introduce a federated learning framework coping with Hierarchical Heterogeneity (H2-Fed), which can notably enhance the conventional pre-trained deep learning model. The framework ex…
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…
Cooperative Intelligent Transport System (C-ITS) is a promising network to provide safety, efficiency, sustainability, and comfortable services for automated vehicles and road infrastructures by taking advantages from participants. However, the components of C-ITS usually generate large amounts of data, which makes it difficult to explore data science. Currently, federated learning has been proposed as an appealing approach to allow users to cooperatively reap the benefits from trained participants. Therefore, in this paper, we propose a novel Semi-asynchronous Hierarchical Federated Learning …
Southern University of Science and Technology（南方科技大学）；The University of Tokyo（东京大学）
Estimating the travel time of a path is an essential topic for intelligent transportation systems. It serves as the foundation for real-world applications, such as traffic monitoring, route planning, and taxi dispatching. However, building a model for such a data-driven task requires a large amount of users' travel information, which directly relates to their privacy and thus is less likely to be shared. The non-Independent and Identically Distributed (non-IID) trajectory data across data owners also make a predictive model extremely challenging to be personalized if we directly apply federate…
University of Technology Sydney（悉尼科技大学）；Universitas Gadjah Mada（加查马达大学）；University College Dublin（都柏林大学学院）；Sun Yat-sen University（中山大学）
Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by leveraging smart vehicles (SVs) to participate in the learning process with minimum data exchanges and privacy disclosure. The collected data and learned knowledge can help the vehicular service provider (VSP) improve the global model accuracy, e.g., for road safety as well as better profits for both VSP and participating SVs. Nonetheless, there exist major challenges when implementing the FL in IoV networks, such as dynamic activities and diverse quality-of-information (QoI) from a large number of SVs, VSP's limited pa…
Federated learning (FL) is a machine learning technique that aims at training an algorithm across decentralized entities holding their local data private. Wireless mobile networks allow users to communicate with other fixed or mobile users. The road traffic network represents an infrastructure-based configuration of a wireless mobile network where the Connected and Automated Vehicles (CAV) represent the communicating entities. Applying FL in a wireless mobile network setting gives rise to a new threat in the mobile environment that is very different from the traditional fixed networks. The thr…
Princeton University（普林斯顿大学）；Shenzhen Research Institute of Big Data（深圳市大数据研究院）；The Chinese University of Hong Kong（香港中文大学）；Virginia Tech（弗吉尼亚理工大学）
Internet of Things (IoT) services will use machine learning tools to efficiently analyze various types of data collected by IoT devices for inference, autonomy, and control purposes. However, due to resource constraints and privacy challenges, edge IoT devices may not be able to transmit their collected data to a central controller for training machine learning models. To overcome this challenge, federated learning (FL) has been proposed as a means for enabling edge devices to train a shared machine learning model without data exchanges thus reducing communication overhead and preserving data …
Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）；Information Technology R&D Center, Mitsubishi Electric Corporation（三菱电机株式会社信息技术研发中心）；NC State University（北卡罗来纳州立大学）
While privacy concerns entice connected and automated vehicles to incorporate on-board federated learning (FL) solutions, an integrated vehicle-to-everything communication with heterogeneous computation power aware learning platform is urgently necessary to make it a reality. Motivated by this, we propose a novel mobility, communication and computation aware online FL platform that uses on-road vehicles as learning agents. Thanks to the advanced features of modern vehicles, the on-board sensors can collect data as vehicles travel along their trajectories, while the on-board processors can trai…
With the incoming introduction of 5G networks and the advancement in technologies, such as Network Function Virtualization and Software Defined Networking, new and emerging networking technologies and use cases are taking shape. One such technology is the Internet of Vehicles (IoV), which describes an interconnected system of vehicles and infrastructure. Coupled with recent developments in artificial intelligence and machine learning, the IoV is transformed into an Intelligent Transportation System (ITS). There are, however, several operational considerations that hinder the adoption of ITS sy…
FedPOD: the deployable units of training for federated learning
Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, South Korea（生物工程跨学科项目、工程学院、首尔国立大学、首尔、韩国）；Department of Nuclear Medicine, College of Medicine, Seoul National University, Seoul, South Korea（核医学系、医学院、首尔国立大学、首尔、韩国）
Centre for Wireless Communications, University of Oulu（奥卢大学无线通信中心）
While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller's available information, measured by the age of information (AoI), is paramount for high-performing industrial automation. The problem in this work is cast as a sensor's transmit power minimization subject to the peak-AoI requirement and a probabilistic constraint on queuing latency. We further characterize the tail behavior of the latency by a generalized Pareto distribution (GPD) for solving the power allocation problem through Lyapunov optimization. As each…
The Chinese University of Hong Kong（香港中文大学）；University of Electronic Science and Technology of China（电子科技大学）；Peng Cheng Lab（鹏城实验室）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios, ranging from risk evaluation systems in finance to cutting-edge technologies like drug discovery in life sciences. However, challenges around data isolation and privacy threaten the trustworthiness of FL systems. Adversarial attacks against data privacy, learning algorithm stability, and system confidentiality are particularly concerning in the context of…
The paper introduces novel families of cruise controllers for autonomous vehicles on lane-free ring-roads. The design of the cruise controllers is based on the appropriate selection of a Control Lyapunov Function expressed on measures of the energy of the system with the kinetic energy expressed in ways similar to Newtonian or relativistic mechanics. The derived feedback laws (cruise controllers) are decentralized (per vehicle), as each vehicle determines its control input based on: (i) its own state; (ii) either only the distance from adjacent vehicles (inviscid cruise controllers) or the sta…
The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning
Department of Pathology and Laboratory Medicine, Indiana University School of Medicine（印第安纳大学医学院病理学与实验室医学系）；Center for Federated Learning in Medicine, Indiana University School of Medicine（印第安纳大学医学院医学联邦学习中心）；Medical AI Group, MLCommons（MLCommons医学人工智能小组）；Intel Corporation（英特尔公司）；Sage Bionetworks（Sage生物网络）；Turku University of Applied Sciences（图尔库应用科学大学）；Stanford University（斯坦福大学）；Ecole Normale Supérieure（巴黎高等师范大学）；Technical University of Munich（慕尼黑技术大学）；Weill Cornell Medicine（韦尔医学院）；Ezri AI Labs（Ezri AI实验室）；City St George’s, University of London（伦敦大学城市学院）；Imperial College London（伦敦帝国学院）；Seoul National University（首尔国立大学）；Ostbayerische Technische Hochschule (OTH) Regensburg（雷根斯堡应用技术大学）；Universität Zürich（苏黎世大学）；Kingston University London（伦敦金史密斯学院）；Departments of Radiology and Imaging Sciences（印第安纳大学医学院放射学与影像科学系；神经外科系；生物统计学与健康数据科学系）；Neurological Surgery（印第安纳大学计算机科学系；Luddy信息学、计算与工程学院）；Biostatistics and Health Data Science, Indiana University School of Medicine；Department of Computer Science, Luddy School of Informatics, Computing and Engineering, Indiana University
Duzce University（杜兹切大学）；University of Luxembourg（卢森堡大学）；Imperial College London（帝国理工学院）；University of Oulu（奥卢大学）
Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, most of these ML applications employ centralized learning (CL), which brings significant overhead for data transmission between the parameter server and vehicular edge devices. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing transmission overhead while achieving privacy through the trans…
University of Sindh（信德大学）；Munster Technological University（芒斯特理工大学）；Nearby Computing（Nearby Computing公司）
The applications concerning vehicular networks benefit from the vision of beyond 5G and 6G technologies such as ultra-dense network topologies, low latency, and high data rates. Vehicular networks have always faced data privacy preservation concerns, which lead to the advent of distributed learning techniques such as federated learning. Although federated learning has solved data privacy preservation issues to some extent, the technique is quite vulnerable to model inversion and model poisoning attacks. We assume that the design of defense mechanism and attacks are two sides of the same coin. …
FedPID: An Aggregation Method for Federated Learning
École Normale Supérieure, PSL University（巴黎高等师范学院、PSL大学）；ezri ai labs（ezri人工智能实验室）；Technical University Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国学院）
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；Department of Electronic Engineering, State Key laboratory of Space Network and Communications, Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学电子工程系、空间网络与通信国家重点实验室、北京信息科学与技术国家研究中心）；Department of Computer Science, Brunel University（布鲁内尔大学计算机科学系）；Department of Electronic Engineering, Shanghai JiaoTong University（上海交通大学电子工程系）；School of Internet of Things Engineering, Wuxi Institute of Technology（无锡职业技术学院物联网工程学院）
Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terrain
Department of Automotive Engineering, Clemson University（汽车工程系，克莱姆森大学）；Ground Vehicle Systems Center, U.S. Army Combat Capabilities Development Command（地面车辆系统中心，美国陆军作战能力发展指挥部）
We present a simplified model of a vehicle driving on a nonplanar road. A parametric surface is used to describe the nonplanar road which can describe any combination of curvature, bank and slope. We show that the proposed modeling approach generalizes planar vehicle models that reference a centerline, such as the Frenet model. We use the proposed approach for vehicle path planning and following using model predictive control. We also model and control vehicle contact with the road surface. We demonstrate that the proposed controller improves speed and lane following on complex roads compared …
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning
Northwestern Polytechnical University（西北工业大学）；Harbin Engineering University（哈尔滨工程大学）；Hong Kong University of Science and Technology（香港科技大学）；Xidian University（西安电子科技大学）；Nanyang Technological University（南洋理工大学）
