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Federated Learning in Vehicular Networks

Ahmet M. Elbir, Burak Soner, Sinem Coleri, Deniz Gunduz, Mehdi Bennis

arXiv:2006.01412v3eess.SPcs.ITcs.LG

TL;DR

Vehicular ML applications face centralized-learning costs from transmitting large raw datasets, motivating federated learning for edge-oriented training. The paper evaluates FL’s feasibility across vehicular applications and case studies, finding reduced transmission overhead but persistent labeling, resource, privacy, and communication challenges.

  • Problem

    Centralized learning for vehicular ML requires transmitting large datasets between vehicles and servers, creating bandwidth, delay, and privacy burdens.

  • Method

    The paper surveys FL feasibility across vehicular applications, analyzes learning and communication challenges, and evaluates CL, FL, and HFCL in case studies.

  • Results

    FL and HFCL reduce transmission overhead relative to CL in the 3D object-detection case study, while FL is also evaluated for mmWave beam selection.

  • Takeaways & Limitations

    FL can support vehicular learning without transmitting whole datasets, but practical deployment depends on labeling, communication, privacy, and edge-resource management.

Abstract

from arXiv · show

Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response. However, most of these ML applications employ centralized learning (CL), which brings significant overhead for data transmission between the parameter server and vehicular edge devices. Federated learning (FL) framework has been recently introduced as an efficient tool with the goal of reducing transmission overhead while achieving privacy through the transmission of model updates instead of the whole dataset. In this paper, we investigate the usage of FL over CL in vehicular network applications to develop intelligent transportation systems. We provide a comprehensive analysis on the feasibility of FL for the ML based vehicular applications, as well as investigating object detection by utilizing image-based datasets as a case study. Then, we identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management. Finally, we highlight related future research directions for FL in vehicular networks.

I. INTRODUCTION

Vehicular networks generate massive sensor datasets, but centralized learning can impose costly bandwidth, delay, and privacy burdens. The paper examines federated learning as an edge-oriented alternative and surveys its applications, feasibility, challenges, and research directions.

  • Autonomous vehicles generate about one gigabyte of sensor data per second from cameras, RADAR, LIDAR, and other sensors.
  • Centralized learning trains neural networks on edge-collected datasets, requiring raw-data transmission that can be costly, delayed, and privacy-invasive.
  • Federated learning exchanges local model gradients with a cloud server, which aggregates them and returns updated parameters instead of collecting whole datasets.
  • The paper analyzes FL across vehicular applications, presents image- and LIDAR-based case studies, and discusses learning, communication, and future-research challenges.

II. ML FOR VEHICULAR APPLICATIONS

Vehicular applications use ML to learn from increasingly abundant and diverse sensor data. The paper organizes these applications into vehicle-management and traffic-management categories.

  • Neural-network ML models have become effective in vehicular applications as sensor data volume and diversity increase.
  • Supervised learning trains neural networks on labeled datasets, whereas unsupervised learning studies clustering without a predefined answer key.
  • The paper classifies ML-based vehicular applications into vehicle management and traffic management.

A. Vehicle Management: Autonomous Driving

Autonomous driving requires layered perception and control tasks supported by sensor data and learned models. Federated learning may better support adaptive higher-level decisions, while accurate online labeling constrains lower-level applications.

  • A. Vehicle Management: Autonomous Driving: Autonomous driving must detect, identify, localize, and track surrounding objects before adjusting vehicle dynamics.
  • A. Vehicle Management: Autonomous Driving: Deep neural networks trained end-to-end on RADAR, LIDAR, and camera data have emerged as alternatives to layered hand-crafted computer-vision methods.
  • A. Vehicle Management: Autonomous Driving: CNN-based autonomous-driving models require large amounts of precisely labeled data, so training is usually conducted offline on a cloud server.
  • A. Vehicle Management: Autonomous Driving: Federated learning can adapt to environmental changes across locations, but its edge gradient computation requires immediate and accurate online labeling.
  • A. Vehicle Management: Autonomous Driving: Higher-level tasks such as intent estimation and driving decisions may benefit more from FL than lower-level object detection and tracking.

III. FL FOR MODEL TRAINING IN VEHICULAR NETWORKS

Centralized training limits local adaptation when vehicular links cannot support raw-data transmission, while FL distributes gradient computation to edge devices. Its benefits depend on fast adaptation, constrained offloading, accurate labeling, and sufficient device resources.

  • III. FL FOR MODEL TRAINING IN VEHICULAR NETWORKS: Centralized training can prevent local data from participating when dynamic channels cannot support its transmission overhead or privacy requirements.
  • III. FL FOR MODEL TRAINING IN VEHICULAR NETWORKS: Federated learning parallelizes mini-batch gradient computation at vehicles, after which a central server combines gradients and broadcasts updated parameters.
  • III. FL FOR MODEL TRAINING IN VEHICULAR NETWORKS: FL is advantageous when applications need rapid adaptation, raw data cannot be timely offloaded, and edge data can be labeled quickly and accurately.
  • III. FL FOR MODEL TRAINING IN VEHICULAR NETWORKS: Vehicular FL remains challenging because relatively few applications satisfy all three conditions for useful FL training.
  • III. FL FOR MODEL TRAINING IN VEHICULAR NETWORKS: FL requires edge computation, so vehicles lacking sufficient computational resources may be excluded from training.
  • III. FL FOR MODEL TRAINING IN VEHICULAR NETWORKS: Hybrid federated and centralized learning lets resource-rich vehicles use FL while others transmit datasets to the parameter server.

A. Case Study 1: 3D Object Detection

The case study evaluates CL, FL, and HFCL for 3D object detection using the Lyft Level 5 AV dataset. FL substantially reduces transmission overhead, while HFCL balances training participation and communication cost.

  • The study evaluates CL, FL, and HFCL for 3D object detection using preprocessed LIDAR and camera data from 10 vehicles.Each local dataset contains 10^3 input-output pairs, and the task has 9 object classes.
  • 4.5 × 10^9 data symbols constitute the approximate CL transmission burden for the object-detection dataset.
  • 28 and 3 times lower transmission overhead are achieved by FL and HFCL, respectively, compared with CL.FL transmits approximately 160 × 10^6 symbols through two-way model-update exchanges over 40 iterations.
  • HFCL provides moderate training performance between CL and FL while allowing all vehicles to participate.HFCL combines federated and centralized learning for clients with differing computational capabilities.

B. Case Study 2: Millimeter-wave Beam Selection

The beam-selection case study uses LIDAR point clouds to select favorable millimeter-wave beams between a moving vehicle and a roadside base station. FL achieves nearly the same accuracy as CL with much lower transmission overhead.

  • The model maps LIDAR point-cloud measurements to the best beam-direction index for V2I millimeter-wave communication.A CNN with 8 layers and 7462 learnable parameters is trained for 20 epochs.
  • 89.1% and 88.6% beam-selection accuracy are reported for CL and FL, respectively.The slight FL performance loss accompanies substantially lower communication overhead.
  • 300 times lower transmission overhead is achieved by FL compared with CL.FL transmits model parameters, whereas CL transmits the whole dataset.

A. FL-Related Research Challenges

FL-related learning challenges arise from decentralized, non-uniform data and the need for labeled training examples. The paper discusses data diversity, limited client participation, and reinforcement learning as an alternative labeling strategy with trade-offs.

  • 1) Data Diversity:: Non-uniform datasets across vehicles create data diversity, increasing averaged-gradient variance and decreasing model convergence rate.Different locations can produce image distributions whose diversity impairs neural-network feature extraction and representation.
  • 1) Data Diversity:: Fewer edge devices in vehicular networks reduce the number of participants available for model aggregation, making training more challenging.
  • 2) Labeling:: Supervised FL training requires labeled datasets, whose annotation demands labor and is commonly performed offline in autonomous-vehicle prototypes.
  • 2) Labeling:: Reinforcement learning avoids labeled data but generally requires longer training times and performs worse than supervised techniques because labels are absent.

3) Efficient Model Training:

Transfer learning can improve model-training efficiency by adapting a pretrained model rather than training from scratch. Its accuracy depends on similarity between newly collected edge data and the pretraining data.

  • 3) Efficient Model Training:: Transfer learning reuses a model trained for one task as the starting point for a different task, reducing training complexity for smaller datasets.Soft parameter updates can train only a small portion of the neural network instead of the full model.
  • 3) Efficient Model Training:: Transfer-learning accuracy depends strongly on similarity between newly collected edge data and the data used to pretrain the model.Higher accuracy with new data may require updating a larger portion of the model, creating a similarity-versus-update trade-off.

1) Transmission Overhead:

Federated learning reduces transmission overhead by sending model updates instead of raw data, but large models can still make updates a bottleneck. Sparsification, quantization, and more reliable communication are proposed to address this trade-off.

  • FL reduces transmission overhead by replacing raw data transmission with model update parameter transmission.
  • Model-update transmission can remain a bottleneck because its size is directly proportional to the learning model’s number of learnable parameters.
  • Sparsification and quantization are methods for reducing transmission overhead in FL-based frameworks.
  • Exploiting gradient sparsity reduces transmitted data but increases encoding and decoding complexity at the parameter server and edge devices.
  • Improved channel reliability can facilitate faster convergence to more accurate models, including through analog links in noisy band-limited channels.

3) Scheduling and Resource Management:

FL convergence depends on heterogeneous communication resources and packet-error performance across participating nodes. The paper identifies explicit scheduling and resource optimization as a communications challenge for vehicular FL.

  • Wireless resource availability and packet-error performance vary across nodes because of device heterogeneity and spatial distribution.
  • FL convergence rate is directly affected by the communication link between each edge device and the central FL controller.
  • Scheduling and resource management of participating nodes must be explicitly optimized to maximize FL convergence.
  • The paper identifies resource management, alongside data rate, reliability, transmission overhead, and privacy, as a major communications challenge for vehicular FL.
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