Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
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DOI：
10.1109/cvprw59228.2023.00553
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Liangqi Yuan;Yunsheng Ma;Lu Su;Ziran Wang
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文献类型：
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作者：
Liangqi Yuan;Yunsheng Ma;Lu Su;Ziran Wang
自然驾驶行为识别（NDAR）已被证明是一种有效的方法来检测驾驶员分心，降低交通事故的风险。然而，机舱内摄像头的侵入性设计引发了对驾驶员隐私的担忧。为了解决这个问题，我们提出了一种新的对等（P2P）联邦学习（FL）框架与持续学习，即FedPC，确保隐私，提高学习效率，同时减少通信，计算和存储开销。我们的框架专注于在无服务器FL框架内解决客户的目标，目标是提供个性化和准确的NDAR模型。我们在两个真实世界的NDAR数据集上展示和评估了FedPC的性能，包括2023年AICity挑战赛中的State Farm Distracted Driver Detection和Track 3 NDAR数据集。我们的实验结果突出了强大的竞争力FedPC相比，传统的客户端到服务器（C2S）FL的性能，知识传播率，并与新客户端的兼容性。
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.