A Survey of Federated Learning for Connected and Automated Vehicles

Chellapandi, Vishnu Pandi; Yuan, Liangqi; Zak, Stanislaw H /.; Wang, Ziran

doi:10.48550/arxiv.2303.10677

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…A thorough review of Federated Learning applications in Connected and Automated Vehicles (CAVs) analyzes data modalities, evaluates various applications, and outlines future research directions [30]. Another study proposes a contextual client selection pipeline for Federated Learning in transportation systems, using Vehicle-to-Everything (V2X) messages to predict latency and select clients accordingly [31].…”

Section: Related Workmentioning

confidence: 99%

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

Kaleem,

Sohail,

Tariq

et al. 2023

Sustainability

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The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively.

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Section: Related Workmentioning

confidence: 99%

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

Kaleem,

Sohail,

Tariq

et al. 2023

Sustainability

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“…b) Networked Learning: The concept of networked learning has been recently explored in the context of connected and autonomous vehicles (CAVs) [11]. In this paradigm, multiple CAVs can cooperate to improve their individual driving policies through message passing and sharing of information [12]- [14].…”

Section: Related Workmentioning

confidence: 99%

Self-Supervised Traffic Advisors: Distributed, Multi-view Traffic Prediction for Smart Cities

Sun

Kousik

Fridovich-Keil

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Connected autonomous vehicles (CAVs) promise to enhance safety, efficiency, and sustainability in urban transportation. However, this is contingent upon a CAV correctly predicting the motion of surrounding agents and planning its own motion safely. Doing so is challenging in complex urban environments due to frequent occlusions and interactions among many agents. One solution is to leverage smart infrastructure to augment a CAV's situational awareness; the present work leverages a recently-proposed "Self-Supervised Traffic Advisor" (SSTA) framework of smart sensors that teach themselves to generate and broadcast useful video predictions of road users. In this work, SSTA predictions are modified to predict future occupancy instead of raw video, which reduces the data footprint of broadcast predictions. The resulting predictions are used within a planning framework, demonstrating that this design can effectively aid CAV motion planning. A variety of numerical experiments study the key factors that make SSTA outputs useful for practical CAV planning in crowded urban environments.

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A Survey of Federated Learning for Connected and Automated Vehicles

Cited by 2 publications

References 41 publications

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

An Improved Big Data Analytics Architecture Using Federated Learning for IoT-Enabled Urban Intelligent Transportation Systems

Self-Supervised Traffic Advisors: Distributed, Multi-view Traffic Prediction for Smart Cities

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