arXiv Machine Learning June 8, 2026 By Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali, Owen Palinski, Allan Salihovic, Dinh Nguyen, Minghui Liwang, Seyyedali Hosseinalipour

Federated Foundation Models over Vehicular Networks

Read the original on arXiv Machine Learning →

arXiv:2606. 06786v1 Announce Type: new Abstract: This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular networks, with the goal of unifying the expressive power of multi-modal multi-task foundation models (M3T FMs) with the privacy-preserving and distributed learning capabilities of federated learning (FL).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

6d ago

Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks

arXiv:2608. 11532v1 Announce Type: cross Abstract: In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy.

By Qasim Zia, Saide Zhu, Haoxin Wang, Zafar Iqbal, Yingshu Li

fine-tuning efficiency safety data-engineering

Hugging Face Trending Papers

Aug 12

Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks

In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET), Federated Learning (FL) has shown its ability to provide data privacy. However, Federated learning struggles to adequately train a global model when confronted with data heterogeneity and data sparsity among vehicles, which ensure suboptimal accuracy in making precise predictions for different vehicle types.

fine-tuning efficiency safety data-engineering

arXiv AI

Jul 28

AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt

arXiv:2512. 24625v3 Announce Type: replace-cross Abstract: Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management.

By Zijian Zhao, Yitong Shang, Sen Li

safety

arXiv AI

Aug 11

Hierarchical Multi-Task Federated Learning in VANETs

arXiv:2608. 08111v1 Announce Type: cross Abstract: Vehicular Ad hoc Networks (VANETs) increasingly rely on federated learning (FL) to enable collaborative intelligence without sharing raw sensory data.

By M. Saeid HaghighiFard, Sinem Coleri

benchmarks

arXiv AI

Jul 7

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

arXiv:2603. 06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications.

By Siyuan Wang, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Kan Zheng

agents reinforcement-learning benchmarks safety

arXiv AI

Jun 16

Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages

arXiv:2606. 16891v1 Announce Type: cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and federated analytics.

By Alvaro Javier Vargas Guerrero, Xinguang Wang, Quang Manh Doan, Guy Nagels

safety

Related stories

Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks

Hierarchical Federated Transfer Learning in Digital Twin-Based Vehicular Networks

AutoFed: Personalized Federated Traffic Prediction via Adaptive Prompt

Hierarchical Multi-Task Federated Learning in VANETs

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages