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

arXiv: https://arxiv.org/abs/2304.07421 · HTML: https://mlsanity.com/paper/2304.07421/

## Key facts

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

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [Federated Learning Enables Big Data for Rare Cancer Boundary Detection](https://mlsanity.com/paper/2204.10836/) (2022)
- [Federated Mutual Learning](https://mlsanity.com/paper/2006.16765/) (2020)
- [Deep Federated Learning for Autonomous Driving](https://mlsanity.com/paper/2110.05754/) (2021)
- [Over-the-Air Decentralized Federated Learning](https://mlsanity.com/paper/2106.08011/) (2021)
- [Decentralized Wireless Federated Learning with Differential Privacy](https://mlsanity.com/paper/2109.09142/) (2021)
- [A Survey of Federated Learning for Connected and Automated Vehicles](https://mlsanity.com/paper/2303.10677/) (2023)
- [On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing](https://mlsanity.com/paper/2303.10695/) (2023)
- [Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a driver](https://mlsanity.com/paper/2204.08096/) (2022)
- [Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification](https://mlsanity.com/paper/2204.13591/) (2022)
- [Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application](https://mlsanity.com/paper/2301.04829/) (2023)
- [When Wireless Communications Meet Computer Vision in Beyond 5G](https://mlsanity.com/paper/2010.06188/) (2020)
- [PEPPER: Empowering User-Centric Recommender Systems over Gossip Learning](https://mlsanity.com/paper/2208.05320/) (2022)

## Built on by (most cited first, 19 of 19)

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey](https://mlsanity.com/paper/2412.13840/) (2024)
- [Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors](https://mlsanity.com/paper/2405.11525/) (2024)
- [The 7th AI City Challenge](https://mlsanity.com/paper/2304.07500/) (2023)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Federated Continual Learning: Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2502.07059/) (2025)
- [Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection](https://mlsanity.com/paper/2401.16685/) (2024)
- [Resource-Constrained Federated Continual Learning: What Does Matter?](https://mlsanity.com/paper/2501.08737/) (2025)
- [FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication](https://mlsanity.com/paper/2310.07048/) (2023)
- [Review of Mathematical Optimization in Federated Learning](https://mlsanity.com/paper/2412.01630/) (2024)
- [Digital Ethics in Federated Learning](https://mlsanity.com/paper/2310.03178/) (2023)
- [Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?](https://mlsanity.com/paper/2411.06618/) (2024)
- [FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classification](https://mlsanity.com/paper/2503.18981/) (2025)
- [Exploring Video-Based Driver Activity Recognition under Noisy Labels](https://mlsanity.com/paper/2504.11966/) (2025)
- [Smart Pressure e-Mat for Human Sleeping Posture and Dynamic Activity Recognition](https://mlsanity.com/paper/2305.11367/) (2023)
- [ReFu: Recursive Fusion for Exemplar-Free 3D Class-Incremental Learning](https://mlsanity.com/paper/2409.12326/) (2024)
- [TriCon-SF: A Triple-Shuffle and Contribution-Aware Serial Federated Learning Framework for Heterogeneous Healthcare Data](https://mlsanity.com/paper/2506.16723/) (2025)
- [DFCA: Decentralized Federated Clustering Algorithm](https://mlsanity.com/paper/2510.15300/) (2025)

## 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.