Dynamic Federated Learning-Based Economic Framework for Internet-of-Vehicles

The Internet-of-Vehicles (IoV) can make driving safer and bring more services to smart vehicle (SV) users. Specif-ically, with IoV, the road service provider (RSP) can collaborate with SVs to provide high-accurate on-road information-based services by implementing federated learning (FL). Nonetheless, SVs' activities are very diverse in IoV networks, e.g., some SVs move frequently while other SVs are occasionally disconnected from the network. Consequently, obtaining information from all SVs for the learning process is costly and impractical. Furthermore, the quality-of-information (QoI) obtained by SVs also dramatically varies. That makes the learning process from all SVs simultaneously even worse when some SVs have low QoI. In this paper, we propose a novel selective FL approach for an IoV network to address these issues. Particularly, we first develop an SV selection method to determine a set of active SVs based on their location significance. In this case, we adopt a K-means algorithm to classify significant and insignificant areas where the SVs are located according to the areas' average annual daily flow of vehicles. From the set of SVs in the significant areas, we select the best SVs for the FL execution based on the SVs' QoI at each learning round. Through simulation results using a real-world on-road dataset, we observe that our proposed approach can converge to the FL results even with only 10% of active SVs in the network. Moreover, our results reveal that the RSP can optimize on-road services with faster convergence up to 63% compared with other baseline FL methods.

The internet of vehicles (IoV), a variant of the traditional VANET, allows real-time data exchange between vehicles, roadside units, parking, and city infrastructure. Nevertheless, the IoV poses many security concerns due to its open nature. Traditional security solutions may not address all the IoV security risks and provide complete protection. Therefore, it is critical to establish trust and to identify dishonest nodes. As a result, trust management-based techniques are also required to improve IoV security. This paper proposes federated learning with a blockchain approach for trust management (FBTM) in IoV. Thus, a vehicular trust evaluation is designed to improve the data acquired for the federated learning model learning process. Moreover, a novel blockchain-based reputation system is developed to guarantee the storage and the share of global federated learning models. In the meanwhile, proof of reputation consensus is proposed to evaluate the roadside units operating as aggregators in the IoV network. Simulation results demonstrate that the proposed scheme is effective for IoV security.

The convergence of vehicular technology, artificial intelligence (AI), and distributed computing has catalyzed the emergence of the Internet of Vehicles (IoV) as a cornerstone of next-generation intelligent transportation systems (ITS). By enabling vehicle-to-everything (V2X) communication, IoV supports cooperative perception, real-time decision-making, and autonomous driving. However, the reliance on large-scale, data-driven intelligence in IoV exposes systems to critical challenges, including adversarial poisoning, privacy leakage, identity forgery, and the fragility of centralized learning architectures. Federated Learning (FL) has been proposed as a promising paradigm to alleviate some of these issues by enabling distributed model training without centralizing sensitive vehicular data. Nonetheless, conventional FL remains vulnerable to security and trust limitations, particularly in dynamic vehicular environments. This thesis addresses these challenges by designing secure, privacy-preserving, and scalable FL frameworks that leverage distributed ledger technologies and cutting-edge security mechanisms.The thesis advances knowledge through four interconnected contributions. First, two novel optimization-driven poisoning attack models are introduced: PA-PSOSA and PAPSOGA, which combine particle swarm optimization with simulated annealing and genetic algorithms, respectively. These models demonstrate that even a small poisoning budget can substantially degrade global model utility under black-box and clean-label constraints, highlighting the urgency of robust defenses in vehicular FL. Second, a permissioned blockchain-enabled FL (BCFL) framework is proposed, in which consortium edge nodes running Practical Byzantine Fault Tolerance (PBFT) consensus replace the central aggregator. With blockchain integration and data validation mechanisms, this design ensures identity authentication, verifiable audit trails, and improved resilience against poisoning and Sybil attacks, while maintaining high model accuracy under adversarial conditions. Third, the framework is further enhanced to achieve inference-resistance by integrating secure aggregation (SecAgg) and differential privacy (DP), and lightweight with off-chain commitments. This design significantly reduces ledger storage requirements, increases system throughput, and mitigates inference-based privacy risks. Finally, to overcome the scalability limitations of PBFT-based BCFL, a DAG-enabled FL (DFL) framework is developed. By leveraging parallel validation, utility-score-based tip selection, and reputation-weighted aggregation, this framework significantly improves scalability, reduces communication complexity, and enhances robustness in asynchronous vehicular environments.Together, these contributions articulate a coherent progression from exposing vulnerabilities in vehicular FL to constructing secure, privacy-preserving, and scalable frameworks tailored for IoV ecosystems. The findings demonstrate that interdisciplinary integration of optimization theory, cryptography, differential privacy, and distributed ledger technologies is indispensable for trustworthy vehicular intelligence. Beyond theoretical significance, the proposed frameworks offer practical designs for deployment in safety-critical IoV environments. Future research directions include the integration of zero-knowledge proofs (ZKP) for verifiable privacy, adaptive defenses against evolving adversarial strategies, and experimental validation in real-world vehicular testbeds. Collectively, this thesis establishes a foundation for secure federated intelligence in IoV, contributing to the reliability, efficiency, and trustworthiness of next-generation ITS.

In the distributed collaborative machine learning (DCML) paradigm, federated learning (FL) recently attracted much attention due to its applications in health, finance, and the latest innovations such as industry 4.0 and smart vehicles. FL provides privacy-by-design. It trains a machine learning model collaboratively over several distributed clients (ranging from two to millions) such as mobile phones, without sharing their raw data with any other participant. In practical scenarios, all clients do not have sufficient computing resources (e.g., Internet of Things), the machine learning model has millions of parameters, and its privacy between the server and the clients while training/testing is a prime concern (e.g., rival parties). In this regard, FL is not sufficient, so split learning (SL) is introduced. SL is reliable in these scenarios as it splits a model into multiple portions, distributes them among clients and server, and trains/tests their respective model portions to accomplish the full model training/testing. In SL, the participants do not share both data and their model portions to any other parties, and usually, a smaller network portion is assigned to the clients where data resides. Recently, a hybrid of FL and SL, called splitfed learning, is introduced to elevate the benefits of both FL (faster training/testing time) and SL (model split and training). Following the developments from FL to SL, and considering the importance of SL, this chapter is designed to provide extensive coverage in SL and its variants. The coverage includes fundamentals, existing findings, integration with privacy measures such as differential privacy, open problems, and code implementation.KeywordsFederated learningSplit learningSplitfed learningDistributed machine learningPrivacySecurityCode examples

A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

Due to the advanced capabilities of the Internet of Vehicles (IoV) components such as vehicles, Roadside Units (RSUs) and smart devices as well as the increasing amount of data generated, Federated Learning (FL) becomes a promising tool given that it enables privacy-preserving machine learning that can be implemented in the IoV. However, the performance of the FL suffers from the failure of communication links and missing nodes, especially when continuous exchanges of model parameters are required. Therefore, we propose the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the communications between the IoV components and the FL server and thus improving the accuracy of the FL. However, a single UAV may not have sufficient resources to provide services for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework to solve the allocation of UAV coalitions to groups of IoV components. Specifically, the coalition formation game is formulated to maximize the sum of individual profits of the UAVs. The joint auction-coalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied to solve the allocation of UAV coalitions. The auction scheme is designed to take into account the preferences of IoV components over heterogeneous UAVs. The simulation results show that the grand coalition, where all UAVs join a single coalition, is not always stable due to the profit-maximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer to support the IoV components independently and not to form any coalition.

In an Internet of Vehicle (IoV) system, federated learning (FL) is a new approach to process real-time vehicle data in a distributed way, which can improve the driving experience and service quality. However, due to the high mobility and uncertainty of vehicles, the existing federated learning protocols are difficult to meet the full requirements from an IoV system, such as efficient resource allocation, high precision learning, and fast convergence of learning algorithm. To solve the above problems, in this paper, we propose a semi-synchronous federated learning (Semi-SynFed) protocol, to improve the performance of machine learning at Internet of Vehicles. In Semi-SynFed, we first select appropriate nodes to participate the aggregation by its computing capacity, network capacity and learning value of training samples. Meanwhile, the dynamic waiting time technique is designed to adjust the server waiting time at each round dynamically, which makes federated learning process work much more efficient. Finally, a dynamic aggregation scheme is designed to aggregate model parameters in an asynchronous way. To evaluate the reliability of Semi-SynFed, we establish a simulation of IoVs based on the real world data. In the comparative experiments, the results show that the Semi-SynFed outperforms the existing federated learning protocols in terms of convergence speed and resource consumption.

In the Internet of Vehicles (IoV) paradigm, a model owner is able to leverage on the enhanced capabilities of Intelligent Connected Vehicles (ICV) to develop promising Artificial Intelligence (AI) based applications, e.g., for traffic efficiency. However, in some cases, a model owner may have insufficient data samples to build an effective AI model. To this end, we propose a Federated Learning (FL) based privacy preserving approach to facilitate collaborative FL among multiple model owners in the IoV. Our system model enables collaborative model training without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in contract theory under information asymmetry. For the latter, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design.

In enhancing transportation, the Internet of Vehicles (IoV) facilitates real-time vehicle-infrastructure interactions, improving traffic management and safety, crucial for autonomous vehicle development. However, high-traffic IoV networks face challenges with traditional Batch Message Verification (BMV) methods, often leading to critical alerts being unverified and dropped due to a lack of prioritization. Addressing this, we introduce an enhanced verification model, “DyBatch: Message Prioritization and Dynamic Batch Verification in Large-scale IoV Networks”, designed for Edge-computing enabled IoV (e-IoV) networks. DyBatch effectively tackles message loss by classifying Basic Alert Messages (BAMs) based on urgency through a flag-based mechanism, coupled with a flexible batch size concept to overcome fixed-size limitations. The model employs Verification-Proxy Vehicles (VPVs), special vehicles that handle verification during Edge Node (EN) bottlenecks. DyBatch aims to minimize message loss and verification delays, enhancing real-time application suitability. Utilizing the Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL) and Java 8 on Jetson Nano and Raspberry Pi 4, our analysis reveals a 90% reduction in verification delays, a 91% improvement in computation overhead, and a 67% increase in verified messages, significantly boosting efficiency in high-density IoV environments.

The Internet of Vehicles (IoV) is an emerging technology that enables vehicles to communicate with their surroundings, provide convenient services, and enhance transportation systems. However, IoV networks can be vulnerable to security attacks because vehicles communicate with other IoV components through an open wireless channel. The recent related work suggested a two-factor-based lightweight authentication scheme for IoV networks. Unfortunately, we prove that the related work cannot prevent various security attacks, such as insider and ephemeral secret leakage (ESL) attacks, and fails to ensure perfect forward secrecy. To address these security weaknesses, we propose an anonymous and efficient authentication scheme with conditional privacy-preserving capabilities in IoV networks. The proposed scheme can ensure robustness against various security attacks and provide essential security features. The proposed scheme ensures conditional privacy to revoke malicious behavior in IoV networks. Moreover, our scheme uses only one-way hash functions and XOR operations, which are low-cost cryptographic operations suitable for IoV. We also prove the security of our scheme using the “Burrows–Abadi–Needham (BAN) logic”, “Real-or-Random (ROR) model”, and “Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool”. We evaluate and compare the performance and security features of the proposed scheme with existing methods. Consequently, our scheme provides improved security and efficiency and is suitable for practical IoV networks.

Advanced connection and autonomous features are being made possible by the Internet of Vehicles (IoV), which is causing a revolution in transportation. Strong security measures are required, however, because the prevalence of connected devices also increases the likelihood of cyberattacks and data breaches. This study introduces a new method for protecting IoV networks, which combines Blockchain technology with Quantum Key Distribution (QKD), creating a security architecture with two layers. Internet of Vehicles (IoV) technologies enable autonomous driving and real-time data exchange by connecting vehicles to infrastructure and one another. These advancements make things safer and more efficient, but they also put sensitive information at risk of cyberattacks. Modern security measures are essential since traditional encryption methods are becoming more and more insecure. To provide encryption that is theoretically unbreakable, the suggested system uses QKD to create and distribute cryptographic keys based on principles of quantum mechanics. To improve trust and transparency, blockchain technology is used to record these keys and any subsequent transactions in an immutable, distributed ledger. A hybrid architecture, with QKD securing the key exchange and Blockchain ensuring the integrity and authenticity of the communication, is designed as part of the integration process. Improved security and speed have been shown in simulations and prototype implementations of the QKD-Blockchain architecture in IoV networks. By preventing eavesdropping and key interception, the QKD technique kept the communication channel secure. With an average delay of only about 2 milliseconds, QKD performed admirably and was well below the permitted range for real-time vehicular communications. On average, validation durations for transactions were 5 milliseconds, which was a little overhead due to blockchain integration. The system efficiently handled up to 10,000 transactions per second without affecting security or performance, proving that it can serve massive IoV networks, according to scalability testing. Under high-load scenarios, the framework maintained consistent performance and security, proving its robustness in stress tests. Together, QKD and Blockchain provide a scalable and trustworthy option for future vehicular communication networks, and these results show how feasible and robust it is to use them to protect IoV systems. An intriguing approach to the security issues plaguing IoV systems is the integration of QKD with Blockchain technology. An unparalleled level of protection against cyber threats is provided by the dual-layered system, which guarantees strong encryption and data integrity. This fresh method may lead to improved and more trustworthy IoV networks by establishing new benchmarks for secure vehicular communication. In order to optimize the implementation and tackle any new issues that may arise, more research and development should be conducted.

The Internet of Vehicles (IoV) enables technology that allows IoV and vehicles to connect everything. IoV has become an essential component of modern life. This exponential growth of IoV technology has introduced significant security and privacy issues, which pose potential threats to different types of attacks and cause different threats to the normal operation of vehicles. To prevent intelligent vehicle accidents and identify malicious attacks within IoV networks, various researchers have focused on machine learning (ML)-based methods to detect attacks. Intrusion detection systems (IDS) are a prominent solution for cyber attacks in IoV using ensemble learning. To achieve higher accuracy and detection rate, designing an improved detection framework using ensemble learning is a challenging task. The design of an ensemble-based IDS depends on two main challenges: selecting base classifiers and their combination methods. Therefore, in this study, we propose a hybrid ML model to detect various attacks in IoV. We have used different ML algorithms to develop an enhanced algorithm that can efficiently detect attacks in IoV networks. To evaluate the performance of the proposed system, we have used two well-known datasets, (CIC-IDS2017) and (UNSW-NB15). The proposed algorithm shows outstanding performance from the performance results, with an average attack detection accuracy of 99.75% and 100% and an F1 score of 99.74% and 100%, respectively, for both datasets. Further performance scores, that is, recall, precision, and F1 score metrics, validate the exceptional effectiveness of the proposed framework.

Due to the dynamic nature of a vehicular fog computing environment, efficient real-time resource allocation in an Internet of Vehicles (IoV) network without affecting the quality of service of any of the onboard vehicles can be challenging. This article proposes a priority-sensitive task offloading and resource allocation scheme in an IoV network, where vehicles periodically exchange beacon messages to inquire about available services and other important information necessary for making the offloading decisions. In the proposed methodology, the vehicles are stimulated to share their idle computation resources with the task vehicles, whereby a deep reinforcement learning algorithm based on soft actor–critic is designed to classify the tasks based on priority and computation size of each task for optimally allocating the power. Furthermore, we also design deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) algorithms for the considered framework. In particular, the algorithms work toward achieving the optimal policy for task offloading by maximizing the mean utility of the considered network. Extensive numerical results under different network conditions, along with comparison among the three algorithms, are presented to validate the feasibility of distributed reinforcement learning for task offloading in future IoV networks.

The introduction of artificial intelligence (AI) tech-nology to Internet of Vehicles (IoV) brings the promising potential of enabling intelligent services to moving vehicles. However, training of conventional centralized AI model suffers from long access latency to cloud server and data privacy leakage of vehicle clients. To address these issues, federated learning (FL) has been introduced to IoV for distributed privacy-preserving model training. Nevertheless, to optimize the system overhead and model accuracy of FL, the selection of federated clients has become a critical problem. To overcome this challenge, a proximal policy optimization (PPO)-based client selection scheme has been proposed in this work for the federated AI model training in IoV. Specifically, the federated client selection problem is formulated as a Markov decision process (MDP) first, and a PPO-based algorithm has been developed for the MDP. Subsequently, the dataflow of the proposed client selection scheme is presented in detail. Simulations have been conducted based on practical scenario settings. Simulation results indicate that the proposed client selection scheme outperforms the double deep Q network (DDQN)-based scheme and biased selection scheme on the con-vergence time and test accuracy of federated model training with more stable system performance.

Many of the machine learning tasks are focused on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) leading to a huge communication overhead. Federated learning (FL) overcomes this issue by allowing the clients to send only the model updates to the PS instead of the whole dataset. In this way, FL brings the learning to edge level, wherein powerful computational resources are required on the client side. This requirement may not always be satisfied because of diverse computational capabilities of edge devices. We address this through a novel hybrid federated and centralized learning (HFCL) framework to effectively train a learning model by exploiting the computational capability of the clients. In HFCL, only the clients who have sufficient resources employ FL; the remaining clients resort to CL by transmitting their local dataset to PS. This allows all the clients to collaborate on the learning process regardless of their computational resources. We also propose a sequential data transmission approach with HFCL (HFCL-SDT) to reduce the training duration. The proposed HFCL frameworks outperform previously proposed non-hybrid FL (CL) based schemes in terms of learning accuracy (communication overhead) since all the clients collaborate on the learning process with their datasets regardless of their computational resources.