Awesome-Federated-Learning-for-Autonomous-Driving

This repository is dedicated to curating a comprehensive collection of research papers that explore the implementation of federated learning in - Autonomous Driving (AD), - Intelligent Transportation Systems (ITS), - and Connected and Automated Vehicles (CAV).

Welcome contributions from everyone in the form of additional relevant papers, as well as suggestions to enhance the repository. Feel free to create issues in this repository or email me. Thank you for your support and collaboration!

Contents

• Main
  • Perception
  • Localization/SLAM
  • Planning
  • Action and Control
  • Behavior Prediction
  • Traffic Flow Prediction
  • V2X Communication
  • Electric Vehicles
  • Blockchain and Privacy
  • Framework
  • Benchmark
  • Survey

• A Federated Learning-Based License Plate Recognition Scheme for 5G-Enabled Internet of Vehicles (Kong, Xiangjie et al., TII 2021) :book:
• Federated Learning for Object Detection in Autonomous Vehicles (Jallepalli, Deepthi et al., BigDataService 2021) :book:
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification (Wang, Shuai et al., IEEE Network 2022) :octocat: :book:
• Efficient Federated Learning With Spike Neural Networks for Traffic Sign Recognition (Xie, Kan et al., TVT 2022) :book:
• FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems (Song, Rui and Xu, Runsheng et al., TIV 2023) :octocat: :book:
• Federated Semi-Supervised Learning for Object Detection in Autonomous Driving (Fangyuan Chi et al., ICASSP 2023) :book:
• Federated Learning for Lidar Super Resolution on Automotive Scenes (Gkillas, Alexandros et al., DSP 2023) :book:
• Federated Cooperative 3D Object Detection for Autonomous Driving (Fangyuan Chi et al., MLSP 2023) :book:
• ADS-Lead: Lifelong Anomaly Detection in Autonomous Driving Systems (Han, Xingshuo et al., TITS 2023) :book:

• FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles (Kong, Xiangjie et al., TCSS 2021) :book:
• FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (Yin, Feng et al., OJSP 2020) :book:
• Reconfigurable Holographic Surface Aided Collaborative Wireless SLAM Using Federated Learning for Autonomous Driving (Zhang, Haobo et al., TIV 2023) :book:

• FED-UP: Federated Deep Reinforcement Learning-based UAV Path Planning against Hostile Defense System (Khalil, Alvi Ataur et al., CNSM 2022) :book:

• Autonomous Braking Algorithm for Rear-End Collision via Communication-Efficient Federated Learning (Liu, Sha et al., GLOBECOM 2021) :book:

• Steering Angle Prediction for Autonomous Driving using Federated Learning: The Impact of Vehicle-To-Everything Communication (M. P. Aparna et al., ICCCNT 2021) :book:

• Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach (Liu, Yi et al., JIOT 2020) :book:
• BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction (Meese, Collin et al., CCGrid 2022) :book:
• FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units (Fang, Shaoheng et al., arXiv 2024) :book:

• Federated Learning for Ultra-Reliable Low-Latency V2V Communications (Lim, Wei Yang Bryan et al., GLOBECOM 2018) :book:
• Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory (Shiri, Hamid et al., TCOMM 2020) :book:
• Multiagent DDPG-Based Deep Learning for Smart Ocean Federated Learning IoT Networks (Kwon, Dohyun et al., JIoT 2020) :book:
• Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (Samarakoon, Sumudu et al., TCOMM 2020) :book:
• Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (Lim, Wei Yang Bryan et al., TITS 2021) :book:
• Federated learning in vehicular networks (Elbir, Ahmet M et al., MeditCom 2022) :book:
• Federated Learning on the Road Autonomous Controller Design for Connected and Autonomous Vehicles (Zeng, Tengchan et al., TWC 2022) :book:
• Cybertwin-Driven Federated Learning Based Personalized Service Provision for 6G-V2X (Sahaya Beni Prathiba et al., TVT 2022) :book:
• A Distributed Learning Architecture for Semantic Communication in Autonomous Driving Networks for Task Offloading (Zheng, Guhan et al., MCOM 2023) :book:

• Energy Demand Prediction with Federated Learning for Electric Vehicle Networks (Saputra, Yuris Mulya M et al., GLOBECOM 2019) :book:
• Federated Learning Meets Contract Theory: Economic-Efficiency Framework for Electric Vehicle Networks (Saputra, Yuris Mulya M et al., TMC 2022) :book:

• Blockchain-based federated learning for device failure detection in industrial IoT (Zhang, Weishan et al., JIOT 2020) :book:
• Federated Learning With Blockchain for Autonomous Vehicles: Analysis and Design Challenges (Pokhrel, Shiva Raj and Choi, Jinho, TCOMM 2020) :book:
• Privacy-preserved federated learning for autonomous driving (Li, Yijing et al., TITS 2021) :book:
• Bift: A Blockchain-Based Federated Learning System for Connected and Autonomous Vehicles (He, Ying et al., JIOT 2022) :book:
• Blockchain-Enabled Federated Learning for UAV Edge Computing Network: Issues and Solutions (Zhu, Chaoyang et al., IEEE Access 2022) :book:
• Federated Vehicular Transformers and Their Federations: Privacy-Preserving Computing and Cooperation for Autonomous Driving (Tian, Yonglin et al., TIV 2022) :book:
• Design of Federated Learning Engagement Method for Autonomous Vehicle Privacy Protection (Jung-Sook Kim, SCIS&ISIS 2022) :book:
• Emergency Vehicle Identification for Internet of Vehicles Based on Federated Learning and Homomorphic Encryption (Siyuan Zeng et al., DDCLS 2023) :book:

• Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach (Ye, Dongdong et al., IEEE Access 2020) :book:
• Real-time end-to-end federated learning: An automotive case study (Zhang, Hongyi et al., COMPSAC 2021) :book:
• End-to-End Federated Learning for Autonomous Driving Vehicles (Zhang, Hongyi et al., IJCNN 2021) :book:
• Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS (Song, Rui et al., ITSC 2022) :book:
• Deep Federated Learning for Autonomous Driving (Nguyen, Anh et al., IEEE IV 2022) :book:
• GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation (Zhang, Zhiwen et al., JIoT 2022) :book:
• Federated Transfer Reinforcement Learning for Autonomous Driving (Liang, Xinle et al., Federated and Transfer Learning 2022) :book:
• A Dispersed Federated Learning Framework for 6G-Enabled Autonomous Driving Cars (Khan, Latif U. et al., TNSE 2022) :book:
• Clustered Vehicular Federated Learning: Process and Optimization (Taïk, Afaf et al., TNSE 2022) :book:
• Communication-Efficient Federated Edge Learning via Optimal Probabilistic Device Scheduling (Zhang, Maojun et al., TWC 2022) :book:
• Complex Network Cognition-based Federated Reinforcement Learning for End-to-end Urban Autonomous Driving (Cai, Yingfeng et al., TTE 2023) :book:
• An Incentive Mechanism of Incorporating Supervision Game for Federated Learning in Autonomous Driving (Fu, Yuchuan et al., TITS 2023) :book:

• FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving (Fantauzzo, Lidia et al., IROS 2022) :octocat: :book:

• Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (Du, Zhaoyang et al., OJCS 2020) :book: