Evaluated in the most challenging scenario characterized by maximum vehicle density, dynamic communication delays, packet loss, severe data Non-IIDness, and complex multi-intersection interactions, the proposed method demonstrates superior collaborative performance.
Abstract
Current distributed decision-making for intelligent connected vehicles is constrained by model heterogeneity, high communication overhead, and data privacy concerns, which impede efficient multi-vehicle collaboration in wireless vehicular communication environments. As reliable information exchange and low-latency signal transmission become increasingly important in intelligent transportation systems and electromagnetic communication networks, this paper proposes a distributed decision-making framework integrating Federated Distillation (FD) and Graph Attention Networks (GAT). A local GAT dynamically constructs vehicle adjacency graphs for neighboring perception, while FD replaces conventional parameter sharing with soft-label exchange, significantly reducing communication burden, enabling knowledge transfer across heterogeneous devices, and alleviating computational bottlenecks on resource-limited nodes. Evaluated in the most challenging scenario characterized by maximum vehicle density, dynamic communication delays, packet loss, severe data Non-IIDness, and complex multi-intersection interactions, the proposed method demonstrates superior collaborative performance. The average decision latency is maintained at 91 ms, which is 43 ms lower than that of the traditional FedAvg approach, while the number of uncoordinated behavior triggers is reduced by 50% to only three. These results verify that the proposed framework achieves efficient and privacy-preserving collaborative decision-making while providing a scalable solution for distributed intelligence in wireless vehicular networks and communication-intensive electromagnetic environments.
The proposed framework is validated by conducting simulation-based experiments on the benchmark datasets and synthetic autonomous workloads, where the novelty lies in the design of the system-level federated learning architecture, instead of the datasets themselves.
Jyotsnarani Tripathy, D. Rajalakshmi, A. N. Ramya Shree et al.· SN Computer Science· 0 citations
The rapid growth of large-scale interconnected systems, such as smart cities, industrial automation, and environmental monitoring, demands intelligent decision-making frameworks that are resilient, scalable, and resource-efficient. Traditional centralized intelligence approaches suffer from communication bottlenecks, high energy demands, and vulnerability to single points of failure, making them unsuitable for realworld deployment. This work introduces an event-triggered decentralized intelligence framework with energy-aware federated learning designed to address these challenges. In the proposed system, distributed nodes collaborate by exchanging model updates only when significant events or anomalies occur, rather than relying on continuous communication. This event-driven strategy substantially reduces bandwidth consumption while enabling timely adaptation to dynamic environments. To further enhance sustainability, the framework integrates energy-aware scheduling, allowing devices with limited power resources to contribute adaptively based on their energy profiles. A multilayer coordination mechanism ensures local autonomy and global consensus without centralized control. Experimental evaluations on representative real-world datasets demonstrate that the proposed method achieves competitive accuracy compared to conventional federated learning while reducing communication overhead by more than 40% and extending device lifetime in energy-constrained settings. Additionally, the framework incorporates Byzantine-resilient aggregation and is analyzed under communication latency and varying network topology conditions.
M. Kishore, N. Velmurugan· 2026 7th International Confe...· 0 citations
Simulation results show that the proposed DH-MDRL framework outperforms conventional schemes without IRSs and achieves an excellent trade-off between V2V link constraints’ satisfaction probability and V2I link sum data rates compared to centralized resource allocation approaches.
An AutoEncoder-based Reliability-Optimized Hierarchical Multi-Task Federated Learning (AERO-HMTFL) framework for dynamic multi-hop clustered VANETs is proposed, which introduces a tri-weighted clustering metric that jointly considers vehicular mobility, shared-model similarity, and task affinity to produce mobility-stable, semantically aligned clusters.
G. S. M. I. M. Saeid HaghighiFard, Fellow Ieee Sinem Coleri, M. S. HaghighiFard· 0 citations
Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable. In this paper, we develop a packet-level transmission framework that captures buffer overflow, delay violations, and transmission errors, and uses the resulting packet delivery ratio (PDR) to represent partial-update reception through a packetized, Bernoulli-masked FL aggregation process. We then formulate a fairness-consensus bilevel (FCB) optimization that jointly controls (i) transmission thresholds to maximize the average PDR while reaching consensus under partial observability and (ii) transmission powers to improve the worst PDR and enforce fairness across IoT learners. To solve this problem, we propose an alternating FCB optimizer composed of a consensus-based threshold controller (CTC), which drives the IoT learners toward a PDR-efficient consensus on transmission thresholds, and a fairness-based power controller (FPC), which updates transmission powers to improve the worst PDR and ensure fairness under the resulting consensus thresholds. Numerical results on CNN-based FL tasks show that the FCB optimizer improves FL aggregation and training performance by enhancing packet-level update delivery, consistently outperforming baseline transmission policies.
Digital Twin-Based Low-Energy Reinforcement Learning for Multi-Cell IoV (DT-LERL) distributed collaborative training architecture in cellular-based IoV scenarios is designed, which allows the twin to replace the end-side vehicular entities by introducing digital twins to carry out scenario interactions and model training.
A. Alamoudi, Abdullah S. Almansouri· Journal of Big Data· 0 citations
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