Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
Open accessAbstract
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.
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Advances such as a DFL framework and gossip protocols in [168] can improve model training in dynamic multi-user environments like passenger monitoring in public transit.
“Additionally, advances like DFL framework and gossip protocols in [168], can improve model training in dynamic multi-user environments like passenger monitoring in public transit.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [168]
Section 3.2 describes FedPC’s gossip protocol as a way to handle dynamic vehicle connections, and the Conclusion reports rapid knowledge dissemination on driving-action datasets (page numbers unavailable). The paper does not study passenger monitoring in public transit, so the claimed benefit in that setting is an extrapolation. (LLM, conf=0.88)
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- Authors
- Yuan, L., Ma, Y., Su, L., & Wang, Z.
- Affiliations
- Purdue University West Lafayette (US)
- Purdue University,West Lafayette,USA
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 5250-5259
- OpenAlex
- W4385815544
- Cited by
- 45
- References
- 40
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 18 · 2023: 4 · 2022: 1 · 2021: 3 · 2020: 1
- FWCI
- 7.09
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- peer-to-peer federated learning, continual learning, driver distraction detection, driver privacy, personalized models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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