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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, VOL. 9, NO. 1, JANUARY 2024 119

Federated Learning for Connected and Automated

Vehicles: A Survey of Existing Approaches

and Challenges

Vishnu Pandi Chellapandi , Member, IEEE, Liangqi Yuan , Graduate Student Member, IEEE,

Christopher G. Brinton

, Senior Member, IEEE,StanislawH.

Zak , Life Member, IEEE,

and Ziran Wang

, Member, IEEE

Abstract—Machine learning (ML) is widely used for key tasks in

Connected and Automated Vehicles (CAV), including perception,

planning, and control. However, its reliance on vehicular data for

model training presents signiﬁcant challenges related to in-vehicle

user privacy and communication overhead generated by massive

data volumes. Federated learning (FL) is a decentralized ML ap-

proach that enables multiple vehicles to collaboratively develop

models, broadening learning from various driving environments,

enhancing overall performance, and simultaneously securing local

vehicle data privacy and security. This survey paper presents a

review of the advancements made in the application of FL for CAV

(FL4CAV). First, centralized and decentralized frameworks of FL

are analyzed, highlighting their key characteristics and method-

ologies. Second, diverse data sources, models, and data security

techniques relevant to FL in CAVs are reviewed, emphasizing their

signiﬁcance in ensuring privacy and conﬁdentiality. Third, speciﬁc

applications of FL are explored, providing insight into the base

models and datasets employed for each application. Finally, existing

challenges for FL4CAV are listed and potential directions for future

investigation to further enhance the effectiveness and efﬁciency of

FL in the context of CAV are discussed.

Index Terms—Federated learning, connected and automated

vehicles, distributed computing, privacy protection, data security.

I. INTRODUCTION

ONNECTED and automated vehicles (CAV) are the key

to future intelligent transportation systems (ITS) that en-

compass both ground and air transportation [1] , [2], [3], [4], [5],

[6], [7], [8], [9]. With the advent of Big Data, the Internet of

Things (IoT), edge computing, and intelligent systems, CAVs

have the potential to improve the overall transportation system

by reducing trafﬁc accidents, congestion, and pollution [10],

[11], [12], [13]. CAVs integrate both Vehicle-to-Vehicle (V2V)

and Vehicle-to-Infrastructure (V2I) communication capabilities,

facilitating an enhanced perception of the environment beyond

Manuscript received 3 October 2023; revised 31 October 2023; accepted 10

November 2023. Date of publication 14 November 2023; date of current version

23 February 2024. (Corresponding author: Ziran Wang.)

The authors are with the College of Engineering, Purdue University, West

Lafayette, IN 47907 USA (e-mail: cvp@purdue.edu; liangqiy@purdue.edu;

cgb@purdue.edu; zak@purdue.edu; ryanwang11@hotmail.com).

Color versions of one or more ﬁgures in this article are available at

https://doi.org/10.1109/TIV.2023.3332675.

Digital Object Identiﬁer 10.1109/TIV.2023.3332675

the direct line of sight [14], [15], [16]. This involves interac-

tion with other vehicles, trafﬁc s ignals, pedestrians, and other

elements of the transportation ecosystem. Furthermore, CAVs

are designed to assume control of driving tasks by the human

operator under certain conditions, using a variety of sensors

and sophisticated machine learning (ML) algorithms to achieve

autonomous operation.

Currently, CAVs are generating a tremendous amount of raw

data, between 20 and 40 TB per day, per vehicle . The various

sources of these data include engine components, electronic con-

trol units (ECU), perception sensors, and vehicle-to-everything

(V2X) communications. This large amount of data is sent to

other vehicles, roadside infrastructures, or the cloud, continu-

ously or periodically for monitoring, prognostics, diagnostics,

and connectivity features [17]. This ﬂow of data has driven

the ﬂourishing deployment and application of ML in CAVs,

including areas such as Advanced Driver-Assistance Systems

(ADAS) [18], automated driving [19],ITS[20], and sustainable

development [21].

A. Motivation

Due to the large amount of data required to train ML models,

concerns have been raised about data security in terms of the

legitimacy of data collection, data misuse, and privacy breaches.

Data collected by various sensors in CAVs, are also considered

private and are subject to stringent privacy protection regulations

in different regions. One such example is the General Data

Protection Regulation (GDPR) in the European Union [22],

which imposes strict requirements and guidelines on the han-

dling and processing of personal data to ensure i ndividuals’

privacy rights are protected. Even with the development of

advanced ML techniques and vehicle connectivity, it has not

been feasible to have a secure framework to collect data from

every vehicle and train an ML model. These limitations led to

the development of a new ML paradigm known as Federated

Learning (FL) [23], [24]. The term Federated Learning (FL) has

been coined by Google [25]. FL was initially used for mobile

keyboard prediction in Gboard [26] to allow multiple mobile

phones to cooperatively and securely train an ML model. FL has

been extensively applied in various ﬁelds such as industry [27],

[28], [29], energy [30], [31], healthcare [32], [33], and more.

See https://www.ieee.org/publications/rights/index.html for more information.

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120 IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, VOL. 9, NO. 1, JANUARY 2024

Fig. 1. Roadmap of this survey paper.

In FL, edge devices/clients only send the gradients or the

learnable parameters to cloud servers rather than sending mas-

sive local datasets i n a centralized learning framework. Cloud

servers perform a secure aggregation of the received gradi-

ents/weights and update the global model parameters that are

transmitted back to clients/edge devices [34]. This procedure,

known as a communication round, continues iteratively until the

convergence criteria are met in the global model optimization.

The key advantage of FL is reducing the strain on the network

while also preserving the privacy of the local data. FL is a

potential candidate that can utilize the data available from each

CAV and develop a robust ML model.

Despite the beneﬁts of V2X communications among CAVs,

the invasion of privacy, accuracy, effectiveness, and commu-

nication resources is an essential concern to be addressed. FL

frameworks have received attention for their natural ability to

preserve privacy by transmitting only model data between the

server and its clients without including local vehicle data. In

particular, the model data packets are smaller than the user

data, thus saving the consumption of communication resources.

Similarly, FL frameworks distribute training tasks to each client,

and the server does not perform training but only aggregates,

which can reduce the computational demand on the server and

improves training efﬁciency. Recently, there have also been

efforts to train a decentralized FL that allows multiple vehi-

cles to collaboratively train a model without needing a central

server [35], [36]. In our ﬁrst survey of FL for CAV (FL4CAV)

presented in [37], we emphasized applications and explored

foundational challenges in the subject. Building upon that con-

ference version, this extended journal paper further delves into

the underlying methodologies, provides a more comprehensive

review of recent developments, and introduces novel insights and

evaluations, thereby presenting a more exhaustive and nuanced

understanding of the ﬁeld.

B. Paper Organization

In this paper, we provide a survey of FL4CAV, including

deployment of various FL frameworks on CAVs, data modal-

ities and security, diverse applications, and key challenges. The

organization of this survey is shown in Fig. 1. The following

topics are covered in this survey:

A systematic review of FL algorithms is conducted, specif-

ically focusing on their deployment in CAVs. Additionally,

we examine the integration of ML models within the FL

framework for CAV applications.

Data modalities and data security considerations in CAVs

are summarized, highlighting the diverse range of multi-

modal data generated by various sensors.

Critical applications of FL4CAV are explored, such as

driver monitoring, steering wheel angle prediction, vehi-

cle trajectory prediction, object detection, motion control

application, trafﬁc ﬂow prediction, and V2X communica-

tions.

Current challenges and future research directions of

FL4CAV are highlighted, such as performance, safety,

fairness, applicability, and scalability. A comparison of our

survey with other related surveys can be found in Table I.

The remainder of this survey is organized as follows. In

Section II, we describe the two main FL frameworks along

with their algorithms. In Section III, we discuss various data

modalities, ML methods used in FL4CAV applications, and FL

data security in CAVs. Section IV reviews various applications

of FL in CAVs. The multi-modal data, algorithms, and datasets

used in the relevant literature are also summarized. Challenges

and potential research areas are discussed in Section V.In

Section VI, we present conclusions of this survey.

II. F

EDERATED LEARNING METHODS

In this section, we describe the FL frameworks in terms of two

categories: centralized FL and decentralized FL. An illustration

of the categories is shown in Fig. 2. In addition, we provide an

overview of the ML techniques that are commonly used as base

models on local devices during the FL process. The steps of this

process can be described as:

1) Global Model Distribution: The edge server disseminates

the global model parameters to K vehicles.

2) Model Update Using Local Data: Each vehicle indepen-

dently trains the ML model using its own local data.

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