## 1 Introduction  
The Internet of Things (IoT) usage is increasing in both industrial and consumer applications. According to a previous estimation, around 50 billion IoT devices have been installed by 2020 [1]. Aside from the healthcare business, one of the key areas where IoT is widely employed is in-vehicle networks. Vehicular networks evolved from initial Vehicular Ad-hoc Networks (VANETs) to Vehicular Sensor Networks (VSN), which are composed of a connected, scalable, talk-able, and sense-able mesh of devices in which vehicles are a key component of the overall Intelligent Transport Infrastructure (ITI) [2, 3, 4, 5].  
ITI emphasizes (i) improving commuter safety, (ii) vehicle communication, (iii) traffic management, and (iv) intelligent decision making. The ITI is split into two sections: infrastructure-oriented and infrastructure-free. The infrastructure-oriented is permanently installed and primarily monitors and controls components such as Road Side Units (RSUs) and infrastructure-free consists of participant automobiles [6, 7].  
Security is the most important necessity of such real-time systems, because a single mistake or problem might result in death. All three pillars of security must be addressed in such systems: (i) Confidentiality, (ii) Integrity, and (iii) Availability. The ITI's network is the most important point of entry. There are three main domains, which are (i) Ad-hoc Network Domain (AHND), (ii) Road Side Network Domain (RSND), and (iii) In-Vehicle Network Domain (IVND). The paramount communication flows among these domains are vehicle to vehicle (V2V) and RSU (V2R). Data transmission across various domains and information are given via the communication channel are critical [8]. Data exchange across various domains and information transmitted through the communication channel are critical in vehicular networks [8]. The VANET's security is directly proportional to the number of reliable components providing accurate and timely data through the network. Major attacks on these domains are likely to include but are not limited to data sniffing, spoofing with malicious intent, malicious data injection, and assaults on data or network availability. In the majority of the cases, one can identify that the attacks are related to data shared on the vehicular network [9]. To ensure a safe and trusted ITI, data integrity, privacy, and confidentiality assurance are the main goals.  
Blockchain and Federated Learning (FL) are two emerging technologies that may be employed in ITI to solve privacy and security concerns while also limiting communication costs and latency problems. Blockchain is a digital ledger for record-keeping [10; 11]. It is an decentralized and dispersed in nature with tamper-resistant and tamper-evident features [12; 13; 14]. Google Inc. introduced the FL idea to solve privacy preservation and communication overhead Issues that emerged from combining data from several nodes and storing it in a centralized location [15; 16]. Machine Learning (ML) and Deep Learning (DL) algorithms provide better outcomes if more data is allocated for the model's training; nevertheless, processing such a large quantity of data takes longer during training. Training with fewer data takes less time and yields a lower accuracy score, according to [17]. Blockchain may provide vehicle integrity and trustworthiness, FL can be used to preserve privacy, and encryption for confidentiality ensures realistic and implementable solutions that can be implemented into the vehicular network. Motivated by these, we have attempted to provide a comprehensive survey on integrating blockchain and FL in vehicular networks in this work.