FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributions

• URL: http://arxiv.org/abs/2401.06953v1
• Date: Sat, 13 Jan 2024 02:15:41 GMT
• Title: FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributions
• Authors: Lin Lu
• Abstract summary: Vehicle-cloud collaboration is proposed as a privacy-friendly alternative to centralized learning. This framework includes a consistently federated version of the scoring method to reduce the performance degradation of the global scoring model.
• Score: 6.195950768412144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scoring the driving performance of various drivers on a unified scale, based on how safe or economical they drive on their daily trips, is essential for the driver profile task. Connected vehicles provide the opportunity to collect real-world driving data, which is advantageous for constructing scoring models. However, the lack of pre-labeled scores impede the use of supervised regression models and the data privacy issues hinder the way of traditionally data-centralized learning on the cloud side for model training. To address them, an unsupervised scoring method is presented without the need for labels while still preserving fairness and objectiveness compared to subjective scoring strategies. Subsequently, a federated learning framework based on vehicle-cloud collaboration is proposed as a privacy-friendly alternative to centralized learning. This framework includes a consistently federated version of the scoring method to reduce the performance degradation of the global scoring model caused by the statistical heterogeneous challenge of local data. Theoretical and experimental analysis demonstrate that our federated scoring model is consistent with the utility of the centrally learned counterpart and is effective in evaluating driving performance.

Related papers

• Cross-Camera Distracted Driver Classification through Feature Disentanglement and Contrastive Learning [13.613407983544427]
  We introduce a robust model designed to withstand changes in camera position within the vehicle. Our Driver Behavior Monitoring Network (DBMNet) relies on a lightweight backbone and integrates a disentanglement module. Experiments conducted on the daytime and nighttime subsets of the 100-Driver dataset validate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-11-20T10:27:12Z)
• Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction [54.23208041792073]
  Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. A key challenge in the ASQP task is the scarcity of labeled data, which limits the performance of existing methods. We propose a self-training framework with a pseudo-label scorer, wherein a scorer assesses the match between reviews and their pseudo-labels.
  arXiv  Detail & Related papers  (2024-06-26T05:30:21Z)
• Towards Robust Federated Learning via Logits Calibration on Non-IID Data [49.286558007937856]
  Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. Recent studies have shown that FL is vulnerable to adversarial examples, leading to a significant drop in its performance. In this work, we adopt the adversarial training (AT) framework to improve the robustness of FL models against adversarial example (AE) attacks.
  arXiv  Detail & Related papers  (2024-03-05T09:18:29Z)
• Robust Training of Federated Models with Extremely Label Deficiency [84.00832527512148]
  Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. We propose a novel twin-model paradigm, called Twin-sight, designed to enhance mutual guidance by providing insights from different perspectives of labeled and unlabeled data. Our comprehensive experiments on four benchmark datasets provide substantial evidence that Twin-sight can significantly outperform state-of-the-art methods across various experimental settings.
  arXiv  Detail & Related papers  (2024-02-22T10:19:34Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Confidence-based federated distillation for vision-based lane-centering [4.071859628309787]
  This paper presents a new confidence-based federated distillation method to improve the performance of machine learning for steering angle prediction. A comprehensive evaluation of vision-based lane centering shows that the proposed approach can outperform FedAvg and FedDF by 11.3% and 9%, respectively.
  arXiv  Detail & Related papers  (2023-06-05T20:16:19Z)
• GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models [60.48306899271866]
  We present a new framework, called GREAT Score, for global robustness evaluation of adversarial perturbation using generative models. We show high correlation and significantly reduced cost of GREAT Score when compared to the attack-based model ranking on RobustBench. GREAT Score can be used for remote auditing of privacy-sensitive black-box models.
  arXiv  Detail & Related papers  (2023-04-19T14:58:27Z)
• Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropout [15.569507252445144]
  Label distribution skew induced data heterogeniety has been shown to be a significant obstacle that limits the model performance in federated learning. We propose a simple yet effective framework by introducing a prior-calibrated softmax function for computing the cross-entropy loss. The improved model performance over existing baselines in the presence of non-IID data and client dropout is demonstrated.
  arXiv  Detail & Related papers  (2023-03-11T05:17:59Z)
• Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction [6.804209932400134]
  This article proposes a comprehensive data clustering framework for a large set of vehicle driving data. Our approach thoroughly considers the traffic elements, including both in-traffic agent objects and map information. With the newly designed driving data clustering evaluation metrics based on data-augmentation, the accuracy assessment does not require a human-labeled data-set.
  arXiv  Detail & Related papers  (2021-03-30T06:22:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.