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. 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
The paper introduces FedQoS, an asynchronous federated learning framework designed for multimodal in‑cabin interaction in smart vehicles. It uses a two‑phase gating mechanism: a resource‑aware training gate that starts local learning only when sensing buffers and energy reserves meet safety thresholds, and a QoS‑aware transmission policy that gates uplink updates based on an efficiency score balancing model novelty, latency, and energy costs. Experiments on vehicular datasets show FedQoS achieves competitive personalized accuracy with only marginal loss compared to FedAvg, while reducing communication overhead by 76.7% and latency cost by 26.0%.
By Baran Can G\"ul, Mert Nak{\i}p, Nasser Jazdi, Michael Weyrich
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
arXiv:2508. 00917v2 Announce Type: replace-cross Abstract: Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in complex environments.
By Jiayuan Wang, Farhad Pourpanah, Q. M. Jonathan Wu, Ning Zhang
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
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
FedPGT introduces a progressive gradient transmission scheme for vehicular federated learning over time‑varying channels, where vehicles send high‑magnitude gradient entries according to instantaneous channel conditions. The authors derive a convergence bound showing diminishing returns governed by a power‑law decay, and formulate a stochastic optimization problem that is solved via a Lyapunov drift‑plus‑penalty approach with per‑slot surrogate variables. A low‑complexity resource allocation algorithm is proposed, and experiments on CIFAR‑10 and Argoverse demonstrate a 3.65% accuracy gain and a 12.66% reduction in displacement error compared to state‑of‑the‑art baselines.
By Jintao Yan, Tan Chen, Yuxuan Sun, Sheng Zhou, Zhisheng Niu
The paper introduces HMS‑SCP, a hierarchical multi‑scale semantic‑aware cooperative perception framework for V2X communication. It uses a spatial importance predictor to select task‑relevant grid elements at multiple scales and maps them directly into complex‑valued symbols for joint source‑channel coding, achieving ultra‑low symbol rates and noise resilience. Experiments on OPV2V and DAIR‑V2X show that HMS‑SCP maintains high‑confidence far‑field detection with sub‑16 ms latency even under severe Rayleigh fading and extreme compression.
By Chun-Yeow Yeoh, Chee Keong Tan, Joanne Mun-Yee Lim, Heng-Siong Lim
arXiv:2606. 04072v1 Announce Type: cross Abstract: Deep learning models are increasingly central to autonomous vehicle (AV) pipelines, yet their integration has traditionally followed a monolithic design where perception, planning, and control execute on a single onboard computer.
By Pragya Sharma, Brian Wang, Mani Srivastava
arXiv:2608. 01426v1 Announce Type: new Abstract: Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performance is fundamentally constrained by heterogeneous data distributions, limited communication resources, and energy availability.
By Furkan Bagci, Busra Tegin, Mohammad Kazemi, Tolga M. Duman