Incremental federated learning for traffic flow classification in heterogeneous data scenarios

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Incremental federated learning for traffic flow classification in heterogeneous data scenarios ; volume:36 ; number:32 ; day:12 ; month:8 ; year:2024 ; pages:20401-20424 ; date:11.2024
Neural computing & applications ; 36, Heft 32 (12.8.2024), 20401-20424, 11.2024

Creator: Pekar, Adrian
Makara, Laszlo Arpad
Biczok, Gergely

Contributor: SpringerLink (Online service)

DOI: 10.1007/s00521-024-10281-4

URN: urn:nbn:de:101:1-2412142114390.325987675371

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:25 AM CEST

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Pekar, Adrian
Makara, Laszlo Arpad
Biczok, Gergely
SpringerLink (Online service)

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Incremental federated learning for traffic flow classification in heterogeneous data scenarios

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Other Objects (12)

Global prototype distillation for heterogeneous federated learning

Probabilistic Maneuver Recognition in Traffic Scenarios

Resource allocation in heterogeneous scenarios

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Fedisp: an incremental subgradient-proximal-based ring-type architecture for decentralized federated learning

Defining Traffic Scenarios for the Visually Impaired

Enabling Teleoperated Driving in Everyday's Traffic Scenarios

ConSolid: A federated ecosystem for heterogeneous multi-stakeholder projects

Minority Game: Experiments and Simulations of Traffic Scenarios

Traffic Data Evaluation for Automated Driving Handover Scenarios

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Global prototype distillation for heterogeneous federated learning

Probabilistic Maneuver Recognition in Traffic Scenarios

Resource allocation in heterogeneous scenarios

Semi-HFL: semi-supervised federated learning for heterogeneous devices

Fedisp: an incremental subgradient-proximal-based ring-type architecture for decentralized federated learning

Defining Traffic Scenarios for the Visually Impaired

Enabling Teleoperated Driving in Everyday's Traffic Scenarios

ConSolid: A federated ecosystem for heterogeneous multi-stakeholder projects

Minority Game: Experiments and Simulations of Traffic Scenarios

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Domain Knowledge Guided Representation Learning for Traffic Scenarios

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Enabling Teleoperated Driving in Everyday's Traffic Scenarios

ConSolid: A federated ecosystem for heterogeneous multi-stakeholder projects

Minority Game: Experiments and Simulations of Traffic Scenarios

Traffic Data Evaluation for Automated Driving Handover Scenarios

Domain Knowledge Guided Representation Learning for Traffic Scenarios

Core network traffic prediction based on vertical federated learning and split learning

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