Federated Learning and Blockchain Applications in Vehicular Networks: Comparison
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Please note this is a comparison between Version 2 by Nora Tang and Version 1 by Thippa Reddy Gadekallu.

The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI.  While transmitting data, valuable information can be disclosed through the model parameters by reverse engineering. The disclosure of valuable data motivated researchers and developers to adopt known security and privacy defense methods, e.g., functional encryption and differential privacy, to FL.

  • blockchain
  • federated learning
  • intelligence transportation system
  • vehicular internet of things

1. Federated Learning and Blockchain for Security in Vehicular Networks

The fundamentals of information security CIA must be adhered to by the FL developers and adopters. Many end devices are included in the exposure and training of model characteristics through a decentralized approach, making FL vulnerable to several open risks and attacks. Current research regarding vulnerabilities and frameworks for mitigating risks in the FL technology is limited.
By using BC technology, several researchers and developers made efforts to improve the security of the VANET in recent times. Reference [131][1] proposed a novel secure spectrum sharing technique for VANET cellular networks based on blockchain; the proposed technique is for VANET and network operators where a new Stackelberg framework is presented for the optimal spectrum approaches. Reference [132][2] presented BC-as-a-service (BaaS) for IoT devices cohesive with MEC. VANET is used as a base station in the proposed framework, and BC is used for computation-intensive task offloading. For IoT networks based on MEC, [133][3] proposed a framework for secure data collection. In the proposed framework for authentication, end devices transfer private data to MEC servers. A BC-based decentralized framework is proposed by [134][4] for the ground to air data sharing in IoT networks. A Cournot framework is designed to achieve maximum advantages from the ground to air sensors. For efficient and secure key distribution and recovery in VANET, [135][5] proposed an essential distribution technique based on the decentralized group by exploiting mutual healing and private BC protocol. For the security of VANET, several frameworks are proposed by different researchers based on the BC technique; some of the proposed frameworks are based on the BC network implementation under the FL framework for the applications of MCS. Reference [136][6] proposed a novel technique of privacy-preserving and secure FL for VANET. The author also presented a decentralized FL framework based on the BC techniques focused on user privacy to protect data contribution verification and data training between VANET. Table 31 provides solutions to FL limitations.
Table 31.
 Blockchain-enabled federated learning.
Limitation of FL Solution Provided by Blockchain-Enabled FL
FL is not suitable for the aggregating

updates while selecting vehicles

and maintain GM.		 Blockchain provides a solution to all these

problems through its decentralized storage

and further maintaining the FL model.

Blockchain can be used to store GM.
High speed is required for the server

to gather information and update

vehicles (clients).
Express bandwidth is required.
Skewing in GM can also be expected

because of biasness.
FL cannot detect the internal attacks by

malicious node while updates are

gathered from every vehicle in a

network causing GM unable to link up.

2. Federated Learning and Blockchain for Privacy Preservation in Vehicular Networks

Shortcomings of VANET are privacy, availability, integrity, identification, and confidentiality prevention from incoming attack [137,138][7][8]. Authentication of each vehicle in a network is a key security feature that must be ensured while spreading data within or across the network. Previously, the identification system was based on Public Key Infrastructure (PKI), where each vehicle in a system exchanges its private encrypted identification message to the Local Authentication Center (LAC), which takes enough time to identify a single vehicle. Periodic encryption and decryption create overhead problems in a network, which in return affect the efficiency and reliability of a network [139][9].
Privacy assurance is becoming the primary concern as technology is intervening in our daily life [140][10]. Due to the limitation of mobility and resources of vehicles in VANET, there are two main problems with deploying special data privacy system [141,142][11][12]. To ensure vehicles’ data privacy and reduce latency, FL enables several entities with fewer resources, e.g., RDUs and vehicles, to combine and train a general model using local data of devices. During the data transformation process, to preserve data privacy, the raw data of the network is distorted by plotting this in different models with less sensitive information [143][13]. Leveraging FL, the integration of two different components can mitigate data privacy problems in VANET [144][14], as can be seen in Figure 51.
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Figure 51.
 Integration of FL and blockchain in the VANET environment.
To address the privacy issues in VANET, various researchers tried to solve the issues from various research angles. Existing privacy frameworks: the differential privacy framework [145][15], its extensions [146][16], and the classic privacy framework are not sufficient to solve the privacy issues in VANET. To date, researchers did not find an optimal global solution for data privacy, and utility protection [147][17]. Ref. [148][18] proposed a encryption-based technique to solve the privacy issues. The proposed technique is helpful to a satisfactory level, but the proposed technique does not help in big data situations. FL can accomplish effective communication by transferring updated data between global models and local models [149,150][19][20]. FL solves the privacy issues to the maximum level, but another issue can arise: if the central model is poisoned or compromised, the adversaries may launch successful attacks. To solve the trust issues in FL, blockchain technology is introduced in such situations [151][21]. By forcefully incorporating privacy solutions through blockchain, it will decrease the efficiency of the application. FL overcame several data privacy issues by dividing the data into two parts: global aggregation and local training in the learning phase, but several other security issues arose. Table 42 presents the literature review of security, privacy, and energy efficiency in the VANET environment.
Table 42.
 Literature review of security, privacy, and energy efficiency.
Ref. Contribution Environment Focused Area
[152][22] In this research work, the author proposed a privacy reserving communication scheme based on VANET. The proposed framework meets the contextual and content privacy requirements. It used identity-based encryption and an elliptic curve cryptography scheme. ITS Security and Privacy
[153][23] In this research work, the author proposed a contest-aware quantification technique to overcome security issues in VANET based on the Markov chain method. VANET Security
[154][24] Based on wireless communication, the author presents a literature review of existing work related to VANET technology. The author also presents research directions and open issues for the integration of SDN with VANET. SDN, IoT Security
[155][25] The proposed work addressed different privacy and security issues regarding VANET. The paper also presents the solutions to privacy and security issues. VANET Security and Privacy
[156][26] The proposed work presented an overview of secure and smart communications using the IoT-based VANET technique to overcome traffic congestions in CPS, known as networks of IoV. CPS, IoV Security
[157][27] The author made different clusters of vehicle packets of the specific cellular tower in an IoT environment. This process simplified communication, and VANET architecture reduces energy consumption and network delays. IoT Energy efficiency
[158][28] The author proposed a lightweight end-to-end security solution for SDNV. The proposed objectives are achieved on two-level: RSU-based authentication technique and personal IDS. The lightweight security solution will also provide privacy. SDNV Energy
[159][29] The author proposed a source location privacy preservation method based on smart energy for sustainable city roads. The proposed technique hides source location based on acceleration, distance, speed, and trust. IoT Energy and Privacy
[160][30] The author proposed a new algorithm for multi-hop transmission called fuzzy clustering routing. The author also analyzed clustering limitations, which are performed through different algorithms. To transfer data, multi-hop routing was used. IoT Energy
[161][31] This paper presented the different notions of blockchain and its usability in IoT networks. The author presented different privacy issues regarding the implementation of blockchain in IoT. The author presented FL usability in IoT networks, privacy risks, and taxonomy. IoT Privacy
[162][32] Among different elements elaborate to manage a group of vehicles containing data, the author proposed a blockchain framework. The author integrates VPKI for blockchain to provide privacy and membership association. VANET Privacy
[163][33] The author presented the fundamentals of IoT and blockchain. Then, the author presented a comprehensive literature review based on blockchain techniques for VIoT through the technical issues and problems. At the end of the paper, the authors present the future research direction regarding VIoT and blockchain. VIoT Energy and Privacy
[164][34] The proposed research work analyzed and described existing supply chain, healthcare, VANET, and IoT access control through blockchain security methods. The author also presents a comprehensive survey regarding blockchain security. IoT Security and Privacy
[165][35] The author proposed a new technique called FL-Block (blockchain FL) to overcome the existing issues in FL privacy. The local learning update is transferred to global learning using blockchain through this technique. Fog computing Privacy

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