The development of artificial intelligence (AI) and self-driving technology is expected to enhance intelligent transportation systems (ITSs) by improving road safety and mobility, increasing traffic flow, and reducing vehicle emissions in the near future. In an ITS, each autonomous vehicle acts as a node with its own local machine learning models, which can be updated using locally collected data. However, for autonomous vehicles to learn effective models, they must be able to learn from data sources provided by other vehicles and infrastructure, utilizing innovative learning methods to adapt to various autonomous driving scenarios. Distributed learning plays a crucial role in implementing these learning tasks in an ITS. This review provides a systematic overview of distributed learning in the field of ITSs. Within an ITS, vehicles can engage in distributed learning by interacting with peers through opportunistic encounters and clustering. This study examines the challenges associated with distributed learning, focusing on issues related to privacy and security in data intelligence sharing, communication quality and speed, and trust. Through a thorough analysis of these challenges, this study presents potential research avenues to address these issues, including the utilization of incentive mechanisms that rely on reputation, the adoption of rapid convergence techniques, and the integration of opportunistic federated learning with blockchain technology.

For the purpose of designing a reasonable control strategy for DOC-assisted DPF regeneration, a mathematical model that describes the thermal phenomenon both in a diesel oxidation catalyst (DOC) and diesel particulate filter (DPF) during regeneration is developed. All boundary conditions of this model are obtained by experiments. The effects of the main exhaust parameters such as exhaust mass flow rate, exhaust temperature, oxygen concentration and emission of reactants are investigated comprehensively. The effects of two main parameters of control strategy, DOC-out temperature and soot loading, are analyzed as well. To quantify the effects of relevant parameters, the fuzzy grey relational analysis method is utilized to evaluate the correlation coefficient of all factors to key indexes of DPF regeneration such as maximum temperature, maximum rate of temperature increase and regeneration duration. The results of this work will greatly reduce the complexity of analysis and enable more rational control strategy design of DOC–DPF regeneration systems.

Opportunistic Federated Learning (OppCL) is a widely adopted distributed learning approach for Intelligent Transportation Systems (ITS). However, the exchange of information among distributed learning parties raises privacy concerns, and OppCL training faces drawbacks such as a high number of iterations, a long training time, and low efficiency. This paper proposes an innovative framework for privacy-preserving reputation-based Opportunistic Federated Learning (PR-OppCL) to address these challenges. Firstly, we introduce a reputation value considering factors like time and model loss. This value guides our incentive mechanism, which encourages high-quality data-seeking clients to join the training process. Secondly, we implement an auction mechanism that allows clients to auction local training tasks to fog nodes, improving efficiency, and addressing performance imbalances. Thirdly, we integrate lightweight cryptographic primitive Oblivious Transfer (OT) into gradient sharing to protect vehicle privacy. Finally, the experiments demonstrate a significant reduction in training rounds and total time, outperforming existing methods.

In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle models while addressing data privacy and trust challenges. We deploy resilient countermeasures, incentivized mechanisms, and a secure gradient distribution to combat single-point failure verification attacks. Additionally, we integrate the Byzantine fault-tolerant algorithm (BFT) into the node verification component of the delegated proof of stake (DPoS) to minimize verification delays. We validate our approach through experiments on the MNIST, SVHN, and CIFAR-10 datasets, showing convergence rates and prediction accuracy comparable to traditional OppCL approaches.

As Artificial Intelligence (AI) progresses, the application of federated learning in areas such as consumer app security and intelligent transportation systems is increasing rapidly. Federated learning allows model training without necessitating the sharing of local data, yet security issues present obstacles to its advancement. This paper presents a federated learning method that merges gradient compression with homomorphic encryption. Firstly, a unique gradient compression technique is proposed to reduce data transfer by compressing the model parameters exchanged among clients. Then, homomorphic encryption is utilized to prevent breaches of gradient privacy. Experimental results demonstrate that our proposed approach has a minimal impact on the accuracy of the global model, while it reduces data transmission and improves the privacy and security of federated learning.