Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles
Southern University of Science and Technology（南方科技大学）；Sifakis Research Institute of Trustworthy Autonomous Systems（思法基可信自主系统研究院）；Pazhou Lab（琶洲实验室）；Harbin Institute of Technology（哈尔滨工业大学）
The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles. This paper proposes a federated learning (FL) based dynamic map fusion framework to achieve high map quality despite unknown numbers of objects in fields of view (FoVs), various sensing and model uncertainties, and missing data labels for online learning. The novelty of this work is threefold: (1) developing a three-stage fusion scheme to predict the number of objects effectively and to fuse multiple local maps with fidelity scores; (2) developing an FL algorithm which fine-tunes feature models (i.e., representation learning networks for feature extraction) distributively by aggregating model parameters; (3) developing a knowledge distillation method to generate FL training labels when data labels are unavailable. The proposed framework is implemented in the Car Learning to Act (CARLA) simulation platform. Extensive experimental results are provided to verify the superior performance and robustness of the developed map fusion and FL schemes.
Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design
Department of Electrical Engineering, Chalmers University of Technology（查尔姆斯理工大学电子工程系）；Centre for Wireless Communications, University of Oulu（奥卢大学无线通信中心）；Department of Electrical Engineering, Yale University（耶鲁大学电子工程系）
MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles
Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）；Beijing Jiaotong University（北京交通大学）；OPPO；Imperial College London（伦敦帝国理工学院）
FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices
Lebanese American University（黎巴嫩美国大学）；Polytechnique Montréal（蒙特利尔理工学院）；Khalifa University（哈利法大学）；Zayed University（扎耶德大学）；New York University（纽约大学）；Mohammad Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models
ENN Group Co.,Ltd.（ENN集团有限公司）；China University of Petroleum（中国石油大学）；Unicom (Shanghai) Industrial Internet Co.,Ltd.（联通（上海）工业互联网有限公司）；Qingdao Windaka Technology Co.,Ltd.（青岛Windaka科技有限公司）
An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning
Federal University of Viçosa (UFV)（维索萨联邦大学）；Federal University of Espírito Santo (UFES)（圣埃斯皮里图联邦大学）；Salvador University (UNIFACS)（萨尔瓦多大学）；Federal University of Ceará (UFC)（塞阿拉联邦大学）；University of São Paulo (USP)（圣保罗大学）；University of Minho (UMinho)（米尼奥大学）
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection
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）；Sony AI（索尼人工智能研究所）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Nanjing University of Science and Technology（南京理工大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61）；Purdue University（普渡大学）；Jinan University（暨南大学）；University of Houston（休斯顿大学）
Intelligent Agents for Auction-based Federated Learning: A Survey
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Department of Electrical and Computer Engineering, The University of British Columbia（不列颠哥伦比亚大学电气与计算机工程系）；Department of Computer Science, Bar-Ilan University（巴伊兰大学计算机科学系）
Federated Learning via Intelligent Reflecting Surface
ShanghaiTech University（上海科技大学）；Xiamen University（厦门大学）；Tsinghua University（清华大学）；Hong Kong University of Science and Technology（香港科技大学）；Peng Cheng Laboratory（鹏城实验室）
Semi-Supervised Federated Learning via Dual Contrastive Learning and Soft Labeling for Intelligent Fault Diagnosis
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；Institute of Artificial Intelligence Research, Jiangsu Second Normal University（江苏第二师范学院人工智能研究所）；Digital Research Institute, ENN Group（ENN集团数字研究院）；School of Computing, Engineering and Mathematical Sciences, La Trobe University（拉筹伯大学计算、工程与数学科学学院）；College of Science and Engineering, James Cook University（詹姆斯库克大学科学与工程学院）
Privacy-Preserving Edge Federated Learning for Intelligent Mobile-Health Systems
Institute of Computer Engineering, Heidelberg University（海德堡大学计算机工程研究所）；Faculty of Computer Engineering, K. N. Toosi University of Technology（霍加·纳赛尔·图西大学计算机工程学院）；Department of Electrical and Information Technology, Lund University（隆德大学电气与信息技术系）
Federated Learning for Industrial Internet of Things in Future Industries
Deakin University（迪肯大学）；CSIRO（联邦科学与工业研究组织）；University of New South Wales（新南威尔士大学）；Nanjing University of Science and Technology（南京理工大学）；Nanyang Technological University（南洋理工大学）；Princeton University（普林斯顿大学）
AugFL: Augmenting Federated Learning with Pretrained Models
School of Cyber Science and Technology, Sun Yat-sen University（中山大学网络空间安全学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Zhongguancun Laboratory（中关村实验室）；Department of Electrical and Computer Engineering, University of California, Davis（加州大学戴维斯分校电气与计算机工程系）
Optimal Rate Adaption in Federated Learning with Compressed Communications
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；School of Computing, Faculty of Science and Engineering, Macquarie University（麦考瑞大学科学与工程学院计算机学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computing Science, Simon Fraser University（西蒙弗雷泽大学计算科学学院）
