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.
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).
By Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali, Owen Palinski, Allan Salihovic, Dinh Nguyen, Minghui Liwang, Seyyedali Hosseinalipour
The paper introduces Encoder‑Sharing Hierarchical Multi‑Task Federated Learning (EN‑HMTFL) for vehicular ad hoc networks, combining cluster‑based hierarchical federated learning with a globally shared encoder and vehicle‑local decoders. This design allows vehicles performing different perception tasks to collaboratively learn a transferable feature representation while keeping raw data and task‑specific decoders local. Experiments on MNIST and GTSRB datasets show that EN‑HMTFL can improve accuracy by up to 24.0% and reduce communication rounds by up to 69 (28.8%) compared to a representation‑sharing benchmark.
By M. Saeid HaghighiFard, Sinem Coleri
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
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
arXiv:2605. 21115v2 Announce Type: replace-cross Abstract: Federated learning (FL) has emerged as a promising paradigm for managing electric vehicle (EV) battery data in intelligent transportation systems (ITS), enabling privacy-preserving tasks such as anomaly detection and capacity estimation.
By Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-Doudane
The paper investigates how federated learning (FL) updates in vehicular edge networks can reveal client identities through gradient-based attacks on inertial sensor data, using the UCI Human Activity Recognition benchmark as a proxy. Experiments show that an honest-but-curious server can identify clients with near-perfect accuracy from unprotected updates. The authors evaluate lightweight defenses—clipping followed by Gaussian noise and ensemble FL—to mitigate this privacy risk while preserving model utility, reporting differential‑privacy budgets and empirical results across multiple attack classifiers and data partitions.
By Ali Akarma (Islamic University of Madinah, Madinah, Saudi Arabia, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia), Toqeer Ali Syed (Islamic University of Madinah, Madinah, Saudi Arabia), Muhammad Khan (University of the West of England, Bristol, U.K), Qurat-ul-ain Mastoi (University of the West of England, Bristol, U.K), Adeel Ahmad (Islamic University of Madinah, Madinah, Saudi Arabia)
arXiv:2606. 26037v1 Announce Type: cross Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation.
By Guangzheng Hu, Patricia Men\'endez, Feng Liu, Mingming Gong, Guanghui Wang, Liuhua Peng
The paper introduces a latent information sharing scheme for federated learning that mitigates client drift by sharing a small amount of hidden‑layer activations. The authors demonstrate both theoretically and empirically that this approach improves training efficiency while maintaining convergence guarantees and data privacy. Compared to existing methods such as FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, the proposed method achieves higher model accuracy within a fixed round budget without adding significant communication overhead.
By Seungjun Lee, Ensieh Khazaei, Dimitrios Hatzinakos, Baturalp Buyukates, Sunwoo Lee
arXiv:2606. 18003v1 Announce Type: cross Abstract: Collective Adaptive Systems (CAS) increasingly rely on machine learning to let each node learn from locally sensed data, aligning its behavior with the surrounding environment.
By Davide Domini, Gianluca Aguzzi, Lorenzo Pellegrini, Mirko Viroli, Lukas Esterle
Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a cloud server. Straggling clients have been a prob...
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