mlsanityData to 7 Oct 2026

Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition

arXiv 2304.07421 ↗First posted 14 Apr 2023cs.LG cs.CVcited by 19

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Abstract (arXiv)

Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents. However, the intrusive design of in-cabin cameras raises concerns about driver privacy. To address this issue, we propose a novel peer-to-peer (P2P) federated learning (FL) framework with continual learning, namely FedPC, which ensures privacy and enhances learning efficiency while reducing communication, computational, and storage overheads. Our framework focuses on addressing the clients' objectives within a serverless FL framework, with the goal of delivering personalized and accurate NDAR models. We demonstrate and evaluate the performance of FedPC on two real-world NDAR datasets, including the State Farm Distracted Driver Detection and Track 3 NDAR dataset in the 2023 AICity Challenge. The results of our experiments highlight the strong competitiveness of FedPC compared to the conventional client-to-server (C2S) FLs in terms of performance, knowledge dissemination rate, and compatibility with new clients.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 24 of its 40 references that are in the map. First posted on arXiv 14 Apr 2023.

Builds on

24 of 40 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2020The Future of Digital Health with Federated Learningcited by 625
  7. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  8. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  9. 2021Towards Personalized Federated Learningcited by 462
  10. 2019Fair Resource Allocation in Federated Learningcited by 389
  11. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
  12. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
12 more
  1. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  2. 2020Federated Mutual Learningcited by 70
  3. 2021Deep Federated Learning for Autonomous Drivingcited by 54
  4. 2021Over-the-Air Decentralized Federated Learningcited by 18
  5. 2021Decentralized Wireless Federated Learning with Differential Privacycited by 18
  6. 2023A Survey of Federated Learning for Connected and Automated Vehiclescited by 17
  7. 2023On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharingcited by 16
  8. 2022Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a drivercited by 15
  9. 2022Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identificationcited by 13
  10. 2023Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Applicationcited by 11
  11. 2020When Wireless Communications Meet Computer Vision in Beyond 5Gcited by 9
  12. 2022PEPPER: Empowering User-Centric Recommender Systems over Gossip Learningcited by 4

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Citing papers per year: 2023: 6; 2024: 6; 2025: 7.

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  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2023Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challengescited by 39
  3. 2024Unleashing the Power of Continual Learning on Non-Centralized Devices: A Surveycited by 23
  4. 2024Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchorscited by 21
  5. 2023The 7th AI City Challengecited by 20
  6. 2025Handling Out-of-Distribution Data: A Surveycited by 14
  7. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  8. 2024Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selectioncited by 10
  9. 2025Resource-Constrained Federated Continual Learning: What Does Matter?cited by 5
  10. 2023FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communicationcited by 4
  11. 2024Review of Mathematical Optimization in Federated Learningcited by 3
  12. 2023Digital Ethics in Federated Learningcited by 2
7 more
  1. 2024Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?cited by 2
  2. 2025FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classificationcited by 1
  3. 2025Exploring Video-Based Driver Activity Recognition under Noisy Labelscited by 1
  4. 2023Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognitioncited by 0
  5. 2024ReFu: Recursive Fusion for Exemplar-Free 3D Class-Incremental Learningcited by 0
  6. 2025TriCon-SF: A Triple-Shuffle and Contribution-Aware Serial Federated Learning Framework for Heterogeneous Healthcare Datacited by 0
  7. 2025DFCA: Decentralized Federated Clustering Algorithmcited by 0