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

2023-04-14 · Liangqi Yuan, Yunsheng Ma, Lu Su, Ziran Wang

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.

📄 PDF Abstract BibTeX arXiv:2304.07421

Code (0)

등록된 구현이 없습니다.

Tasks

Action Recognition Continual Learning Federated Learning

A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions

2023-06-03 · Hao Wang, Ruihong He, XiaoYu Zhang, Zhaoying Bian 외

Deep learning techniques have been widely used in computed tomography (CT) but require large data sets to train networks. Moreover, data sharing among multiple institutions is limited due to data privacy constraints, whi…

Computed Tomography (CT)Continual Learning Federated Learning

Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification

2022-04-26 · Yixing Huang, Christoph Bert, Stefan Fischer, Manuel Schmidt 외

Due to data privacy constraints, data sharing among multiple centers is restricted. Continual learning, as one approach to peer-to-peer federated learning, can promote multicenter collaboration on deep learning algorithm…

Continual Learning Federated Learning Sensitivity

A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration

2023-09-29 · Yixing Huang, Christoph Bert, Ahmed Gomaa, Rainer Fietkau 외

Due to data privacy constraints, data sharing among multiple clinical centers is restricted, which impedes the development of high performance deep learning models from multicenter collaboration. Naive weight transfer me…

Continual Learning Federated Learning Incremental Learning Transfer Learning

Deep Federated Learning for Autonomous Driving

2021-10-12 · Anh Nguyen, Tuong Do, Minh Tran, Binh X. Nguyen 외

Autonomous driving is an active research topic in both academia and industry. However, most of the existing solutions focus on improving the accuracy by training learnable models with centralized large-scale data. Theref…

Autonomous Driving Federated Learning

PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale

2024-05-28 · Alka Luqman, Shivanshu Shekhar, Anupam Chattopadhyay

This work integrates peer-to-peer federated learning tools with NS3, a widely used network simulator, to create a novel simulator designed to allow heterogeneous device experiments in federated learning. This cross-platf…

Federated Learning