FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units

• URL: http://arxiv.org/abs/2401.12862v2
• Date: Sun, 11 Aug 2024 04:17:09 GMT
• Title: FedRSU: Federated Learning for Scene Flow Estimation on Roadside Units
• Authors: Shaoheng Fang, Rui Ye, Wenhao Wang, Zuhong Liu, Yuxiao Wang, Yafei Wang, Siheng Chen, Yanfeng Wang,
• Abstract summary: Roadside unit (RSU) can significantly improve the safety and robustness of autonomous vehicles through Vehicle-to-Everything (V2X) communication. Currently, the usage of a single RSU mainly focuses on real-time inference and V2X collaboration. Integrating the vast amounts of data from numerous RSUs can provide a rich source of data for model training. We introduce FedRSU, an innovative federated learning framework for self-supervised scene flow estimation.
• Score: 50.930389484343515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Roadside unit (RSU) can significantly improve the safety and robustness of autonomous vehicles through Vehicle-to-Everything (V2X) communication. Currently, the usage of a single RSU mainly focuses on real-time inference and V2X collaboration, while neglecting the potential value of the high-quality data collected by RSU sensors. Integrating the vast amounts of data from numerous RSUs can provide a rich source of data for model training. However, the absence of ground truth annotations and the difficulty of transmitting enormous volumes of data are two inevitable barriers to fully exploiting this hidden value. In this paper, we introduce FedRSU, an innovative federated learning framework for self-supervised scene flow estimation. In FedRSU, we present a recurrent self-supervision training paradigm, where for each RSU, the scene flow prediction of points at every timestamp can be supervised by its subsequent future multi-modality observation. Another key component of FedRSU is federated learning, where multiple devices collaboratively train an ML model while keeping the training data local and private. With the power of the recurrent self-supervised learning paradigm, FL is able to leverage innumerable underutilized data from RSU. To verify the FedRSU framework, we construct a large-scale multi-modality dataset RSU-SF. The dataset consists of 17 RSU clients, covering various scenarios, modalities, and sensor settings. Based on RSU-SF, we show that FedRSU can greatly improve model performance in ITS and provide a comprehensive benchmark under diverse FL scenarios. To the best of our knowledge, we provide the first real-world LiDAR-camera multi-modal dataset and benchmark for the FL community.

Related papers

• Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Models [0.2999888908665658]
  Federated Learning (FL) has become an attractive approach to collaboratively train Machine Learning (ML) models. This work proposes a novel unsupervised FL approach for the identification of potential misbehavior in vehicular environments. We leverage the computing capabilities of public cloud services for model aggregation purposes.
  arXiv  Detail & Related papers  (2024-05-16T08:49:50Z)
• Adaptive Model Pruning and Personalization for Federated Learning over Wireless Networks [72.59891661768177]
  Federated learning (FL) enables distributed learning across edge devices while protecting data privacy. We consider a FL framework with partial model pruning and personalization to overcome these challenges. This framework splits the learning model into a global part with model pruning shared with all devices to learn data representations and a personalized part to be fine-tuned for a specific device.
  arXiv  Detail & Related papers  (2023-09-04T21:10:45Z)
• Benchmarking FedAvg and FedCurv for Image Classification Tasks [1.376408511310322]
  This paper focuses on the problem of statistical heterogeneity of the data in the same federated network. Several Federated Learning algorithms, such as FedAvg, FedProx and Federated Curvature (FedCurv) have already been proposed. As a side product of this work, we release the non-IID version of the datasets we used so to facilitate further comparisons from the FL community.
  arXiv  Detail & Related papers  (2023-03-31T10:13:01Z)
• FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings [51.09574369310246]
  Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models. We propose a novel cross-silo dataset suite focused on healthcare, FLamby, to bridge the gap between theory and practice of cross-silo FL. Our flexible and modular suite allows researchers to easily download datasets, reproduce results and re-use the different components for their research.
  arXiv  Detail & Related papers  (2022-10-10T12:17:30Z)
• Online Data Selection for Federated Learning with Limited Storage [53.46789303416799]
  Federated Learning (FL) has been proposed to achieve distributed machine learning among networked devices. The impact of on-device storage on the performance of FL is still not explored. In this work, we take the first step to consider the online data selection for FL with limited on-device storage.
  arXiv  Detail & Related papers  (2022-09-01T03:27:33Z)
• On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator [32.61132332561498]
  We envision a federated learning (FL) scenario in service of amending the performance of autonomous road vehicles. We focus on the issue of accelerating the learning of a particular class of critical object (CO), that may harm the nominal operation of an autonomous vehicle.
  arXiv  Detail & Related papers  (2021-08-05T16:25:48Z)
• Over-the-Air Federated Learning from Heterogeneous Data [107.05618009955094]
  Federated learning (FL) is a framework for distributed learning of centralized models. We develop a Convergent OTA FL (COTAF) algorithm which enhances the common local gradient descent (SGD) FL algorithm. We numerically show that the precoding induced by COTAF notably improves the convergence rate and the accuracy of models trained via OTA FL.
  arXiv  Detail & Related papers  (2020-09-27T08:28:25Z)
• A Principled Approach to Data Valuation for Federated Learning [73.19984041333599]
  Federated learning (FL) is a popular technique to train machine learning (ML) models on decentralized data sources. The Shapley value (SV) defines a unique payoff scheme that satisfies many desiderata for a data value notion. This paper proposes a variant of the SV amenable to FL, which we call the federated Shapley value.
  arXiv  Detail & Related papers  (2020-09-14T04:37:54Z)
• Evaluation Framework For Large-scale Federated Learning [10.127616622630514]
  Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model. In this paper, we introduce a framework designed for large-scale federated learning which consists of approaches to generating dataset and modular evaluation framework.
  arXiv  Detail & Related papers  (2020-03-03T15:12:13Z)

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.