Federated cooperative detection of anomalous vehicle trajectories at intersections

Koetsier, Christian; Fiošina, Jeļena; Gremmel, Jan N.; Sester, Monika; Müller, Jörg P.; Woisetschläger, David M.

doi:10.1145/3486626.3493439

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…(Yang et al 2019) and (Kairouz et al 2021) give an excellent overview. FL has diverse applications in transportation (Li et al 2022;Wang et al 2021;Koetsier et al 2021). In particular, FL benefits from the abundance of transportation sensor data, enabling, e.g., predictive maintenance systems (Manias and Shami 2021;Bemani and Björsell 2022).…”

Section: Related Workmentioning

confidence: 99%

Data Disparity and Temporal Unavailability Aware Asynchronous Federated Learning for Predictive Maintenance on Transportation Fleets

Von Wahl,

Heidenreich,

Mitra

et al. 2024

AAAI

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Predictive maintenance has emerged as a critical application in modern transportation, leveraging sensor data to forecast potential damages proactively using machine learning. However, privacy concerns limit data sharing, making Federated learning an appealing approach to preserve data privacy. Nevertheless, challenges arise due to disparities in data distribution and temporal unavailability caused by individual usage patterns in transportation. In this paper, we present a novel asynchronous federated learning approach to address system heterogeneity and facilitate machine learning for predictive maintenance on transportation fleets. The approach introduces a novel data disparity aware aggregation scheme and a federated early stopping method for training. To validate the effectiveness of our approach, we evaluate it on two independent real-world datasets from the transportation domain: 1) oil dilution prediction of car combustion engines and 2) remaining lifetime prediction of plane turbofan engines. Our experiments show that we reliably outperform five state-of-the-art baselines, including federated and classical machine learning models. Moreover, we show that our approach generalises to various prediction model architectures.

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Section: Related Workmentioning

confidence: 99%

Data Disparity and Temporal Unavailability Aware Asynchronous Federated Learning for Predictive Maintenance on Transportation Fleets

Von Wahl,

Heidenreich,

Mitra

et al. 2024

AAAI

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show abstract

“…They design a semi-supervised FL framework that first employs the pseudo-labeling method to label local data before grouping and aggregating according to the class distribution as a standard to solve the Non-IID problem. In [72], the authors optimize the one-class support vector machine with stochastic gradient descent (SGD), so that the algorithm supports sequential learning, it can be utilized in FL to detect abnormal vehicle trajectories at intersections, thereby reducing the burden of local data labels.…”

Section: B Traffic Status Identificationmentioning

confidence: 99%

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Zhu

Yin

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Towards federated learning: An overview of methods and applications

Silva

Vinagre

Gama

2023

WIREs Data Min & Knowl

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Federated learning (FL) is a collaborative, decentralized privacy-preserving method to attach the challenges of storing data and data privacy. Artificial intelligence, machine learning, smart devices, and deep learning have strongly marked the last years. Two challenges arose in data science as a result. First, the regulation protected the data by creating the General Data Protection Regulation, in which organizations are not allowed to keep or transfer data without the owner's authorization. Another challenge is the large volume of data generated in the era of big data, and keeping that data in one only server becomes increasingly tricky. Therefore, the data is allocated into different locations or generated by devices, creating the need to build models or perform calculations without transferring data to a single location. The new term FL emerged as a sub-area of machine learning that aims to solve the challenge of making distributed models with privacy considerations. This survey starts by describing relevant concepts, definitions, and methods, followed by an indepth investigation of federated model evaluation. Finally, we discuss three promising applications for further research: anomaly detection, distributed data streams, and graph representation.

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Federated cooperative detection of anomalous vehicle trajectories at intersections

Cited by 6 publications

References 35 publications

Data Disparity and Temporal Unavailability Aware Asynchronous Federated Learning for Predictive Maintenance on Transportation Fleets

Data Disparity and Temporal Unavailability Aware Asynchronous Federated Learning for Predictive Maintenance on Transportation Fleets

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Towards federated learning: An overview of methods and applications

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