VTC 2025 Spring Conference’s Shorts

Fast & Energy Efficient Federated Learning Using Multi-Attribute Client Clustering and Sel...

Federated Learning (FL) presents a promising paradigm for decentralized model training; however, its real-world adoption is hindered by several critical challenges, including non-independent and identically distributed (non-IID) data across clients, heterogeneous computational capabilities, and significant communication overhead. To address these issues, this paper introduces a novel multi-attribute client clustering and selection framework for FL. The proposed approach groups clients according to data distribution, device capabilities, geographic location, and model update behavior. Within each cluster, an adaptive client selection mechanism leverages dynamic attributes such as residual energy, data freshness, and client participation motivation to identify the most suitable participants. Experimental evaluations on standard FL benchmark datasets demonstrate that the proposed framework achieves faster convergence, higher global model accuracy, and improved energy efficiency compared to state-of-the-art approaches.Fast & Energy Efficient Federated Learning Using Multi-Attribute Client Clustering and SelectionMaryam Ben Driss, University of Quebec at Montreal; Essaid Sabir, Teluq University; Halima Elbiaze, University of Quebec a Montreal; Abdoulaye Baniré Diallo, University of Quebec at Montreal

Dynamic Adaptive Federated Learning for mmWave Sector Selection

Beamforming techniques use massive antenna arrays to formulate narrow Line-of-Sight signal sectors to address the increased signal attenuation in millimeter Wave (mmWave). However, traditional sector selection schemes involve extensive searches for the highest signal strength sector, introducing extra latency and communication overhead. This paper introduces a dynamic layer-wise and clustering-based federated learning (FL) algorithm for beam sector selection in autonomous vehicle networks called enhanced Dynamic Adaptive FL (eDAFL). The algorithm detects and selects the most important layers of a machine learning model for aggregation in FL process, significantly reducing network overhead and failure risks. eDAFL also consider an intra-cluster and inter-cluster approach to reduce overfitting and increase the abstraction level. We evaluate eDAFL on a real-world multi-modal dataset, demonstrating improved model accuracy by approximately 6.76% compared to existing methods, while reducing inference time by 84.04% and model size up to 52.20%.Dynamic Adaptive Federated Learning for mmWave Sector SelectionLucas Pacheco, Federal University of Pará; Torsten Braun, University of Bern; Kaushik Chowdhury, Northeastern University, USA; Denis Rosario, Federal University of Pará (UFPA); Batool Salehi, Northeastern University; Eduardo Cerqueira, Federal University of Para

A Critical Learning Period-Aware Incentive Mechanism for Federated Learning

Critical learning periods (CLPs) in federated learning (FL) represent early stages where low-quality contributions (e.g., sparse training data availability) can permanently impair learning outcomes. Yet, strategies to motivate clients with high-quality contributions to join the model training process and share model updates during CLPs remain underexplored. Additionally, existing incentive mechanisms in FL treat all training periods equally, which consequently fails to motivate clients to participate early. In this paper, we propose a time-aware incentive mechanism, called R3T, to encourage client involvement, especially during CLPs in FL. We first characterize the cloud utility as a function of client’s time and system capabilities, effort, joining time, and reward. Then, we analytically derive the optimal contract and devise a CLP-aware mechanism to incentivize early participation and efforts while maximizing cloud utility, even under information asymmetry. By providing the right reward at the right time, R3T can attract the highest-quality contributions during CLPs. Simulation studies show that R3T increases cloud utility and is more economically effective than benchmarks.A Critical Learning Period-Aware Incentive Mechanism for Federated LearningThanh Linh Nguyen, Trinity College Dublin; Viet Quoc Pham, University of Dublin

Deep Reinforcement Learning-Based Joint Optimization of Routing and Wavelength Assignment ...

Low earth orbit (LEO) satellite networks are becoming increasingly important for the development of sixth-generation mobile communication systems along with the concept of integrated space-air-ground networks. However, LEO satellite networks face numerous challenges due to their dynamic topology, large number of satellite nodes, unbalanced load distribution, and differences in communication capabilities. These factors make it difficult for traditional routing strategies to effectively handle the complex network environment and meet the requirements for low latency. To solve these problems, this paper proposes a deep reinforcement learning (DRL)-based routing algorithm for LEO satellite networks, aimed at minimizing the overall end-to-end delay through joint optimization of routing and wavelength resources. The algorithm combines DRL with a wavelength switching model and queuing theory to dynamically optimize routing decisions in response to the existing network state. Simulation results demonstrate that the algorithm exhibits strong convergence properties and effectively reduces full-path delay compared to traditional routing algorithms.Deep Reinforcement Learning-Based Joint Optimization of Routing and Wavelength Assignment for Satellite Optical NetworksYang Gu, Shanghai Advanced Research Institute, Chinese Academy of Sciences; Tianheng Xu, Chinese Academy of Sciences; Xianfu Chen, Shenzhen CyberAray Network Technology Co., Ltd.; Ting Zhou, School of Microelectronics, Shanghai University; Honglin Hu, Shanghai Advanced Research Institute

Reinforcement Learning for Joint Caching and Power Control for IIoT Networks

Minimizing the Age of Information (AoI) violation probability is essential for reliable and timely data delivery in MEC-enabled IIoT networks. This study proposes the Joint Caching and Power Control (JCPC) scheme, leveraging Deep Deterministic Policy Gradient (DDPG) to optimize caching decisions and power allocation under system-level AoI and power constraints. Simulations validate that JCPC outperforms baseline strategies, significantly reducing AoI violation probability and improving the efficiency of MEC-enabled IIoT networks.Reinforcement Learning for Joint Caching and Power Control for IIoT NetworksRitabrata Maiti, Nanyang Technological University (NTU); A.S. Madhukumar, Nanyang Technological University; Ernest Tan, Singapore Institute of Technology

Joint Deployment and Beamforming Optimization for Aerial RIS-Assisted MU-MISO Systems Usin...

Reconfigurable intelligent surfaces (RIS) have emerged as a transformative technology for enhancing wireless coverage and transmission rates while reducing hardware costs and power consumption. This work addresses the limitations of separately optimizing RIS deployment and beamforming by proposing a unified joint deployment and beamforming framework tailored for multi-user multi-input single-output systems. By formulating RIS control as a Markov decision process, we develop a deep reinforcement learning framework that integrates a graph neural network to exploit the inherent topology of wireless communication networks. To reduce the action space and improve learning efficiency, the framework leverages discrete Fourier transform codebooks. Simulation results demonstrate that the proposed approach achieves up to a twofold improvement in weighted sum rate compared to fixed RIS deployment strategies, all while eliminating the need for explicit cascaded channel estimation and accurate channel model.Joint Deployment and Beamforming Optimization for Aerial RIS-Assisted MU-MISO Systems Using Deep Reinforcement LearningWeijie Jin, Jing Zhang, Southeast University; Chao-Kai Wen, National Sun Yat-Sen University, Taiwan; Shi Jin, Southern University

DRFSL: Deep Reinforced Federated Split Learning for Multi-Modal Beamforming in IoV

In Vehicle-to-Everything (V2X) communication, advanced beamforming techniques address signal attenuation caused by mmWave, which provides high bandwidth and low latency. Multi-modal beamforming using Federated Learning (FL) can leverage resources like GPS, Lidar, and image data, significantly accelerating beam searching while enhancing data privacy. The heterogeneity of vehicles, however, affects the availability of computing resources for training machine learning models. Moreover, the multi-modal fusion network may contain billions of parameters, leading to extended training time for FL. To address these challenges, this paper proposes a novel Deep Reinforced Federated Split Learning framework (DRFSL) tailored for multi-modal beamforming with different sub-model architectures. DRFSL efficiently utilizes MEC computing and adapts the collaborative and distributed training to dynamic network conditions and system heterogeneity by incorporating deep reinforcement learning and split learning with FL. Experimental evaluation using real-world datasets demonstrates that DRFSL minimizes average training time by 49.45% and inference time by 24.43% and can achieve higher accuracy within the same timeframe compared to the existing FLASH framework.DRFSL: Deep Reinforced Federated Split Learning for Multi-Modal Beamforming in IoVJINXUAN CHEN, Eric Samikwa, Torsten Braun, University of Bern; Kaushik Chowdhury, University of Texas at Austin

DL-AMC: Deep Learning for Automatic Modulation Classification

Automatic Modulation Classification (AMC) is a signal processing technique widely used at the physical layer of wireless systems to enhance spectrum utilization efficiency. In this work, we propose a fast and accurate AMC system, termed DL-AMC, which leverages deep learning techniques. Specifically, DL-AMC is built using convolutional neural network (CNN) architectures, including ResNet-18, ResNet-50, and MobileNetv2. To evaluate its performance, we curated a comprehensive dataset containing various modulation schemes. Each modulation type was transformed into an eye diagram, with signal-to-noise ratio (SNR) values ranging from -20 dB to 30 dB. We trained the CNN models on this dataset to enable them to learn the discriminative features of each modulation class effectively. Experimental results show that the proposed DL-AMC models achieve high classification accuracy, especially in low SNR conditions. These results highlight the robustness and efficacy of DL-AMC in accurately classifying modulations in challenging wireless environments.DL-AMC: Deep Learning for Automatic
Modulation ClassificationFaheem Ur Rehman, Fast National Univerisity of Computer and Emerging Sciences

Deep Reinforcement Learning-based End-to-End Network Slicing Deployment

Network slicing promotes the development of different industries by dividing multiple logical networks on the same physical network to provide the customized services. However, the complex environment of network slicing deployment makes it difficult to obtain the accurate mathematical models, so that the traditional rule-based heuristic algorithms are difficult to process them efficiently. Therefore, we combine the Graph Convolutional Networks (GCN) and the Deep Deterministic Policy Gradient (DDPG) algorithms and propose the GCN-DDPG (G-DDPG) algorithm in this paper to solve the end-to-end network slicing deployment problem, while taking into account the constraints of Virtual Network Function (VNF) placement, VNF sharing, tolerable latency, and node and link resources limitations. First, the end-to-end network slicing deployment optimization is formulated as a problem of maximizing the weighted sum of system resource utilization and acceptance rate. Second, the physical network features extracted by GCN are combined with the state information of end-to-end network slicing requests as the state space of the optimization problem, and a G-DDPG algorithm is proposed to solve it. Finally, the simulation results demonstrate that our proposed method superior to benchmark solutions in terms of the resource utilization of the system, acceptance rate of end-to-end network slicing.Deep Reinforcement Learning-based End-to-End Network Slicing DeploymentSixue Chen, Miaoyu Lin, Yunfeng Wang, Guorong Zhou, Fanqi Yu, Liqiang Zhao, Xidian University

Deep Learning-Based CKM Construction with Image Super-Resolution

Channel knowledge map (CKM) is a novel technique for achieving environment awareness, and thereby improving the communication and sensing performance for wireless systems. A fundamental problem associated with CKM is how to construct a complete CKM that provides channel knowledge for a large number of locations based solely on sparse data measurements. This problem bears similarities to the super-resolution (SR) problem in image processing. In this paper, we propose an effective deep learning-based CKM construction method that leverages the image SR network known as SRResNet. Unlike most existing studies, our approach does not require any additional input beyond the sparsely measured data. In addition to the conventional path loss map construction, our approach can also be applied to construct channel angle maps (CAMs), thanks to the use of a new dataset called CKMImageNet. The numerical results demonstrate that our method outperforms interpolation-based methods such as nearest neighbour and bicubic interpolation, as well as the SRGAN method in CKM construction. Furthermore, only 1/16 of the locations need to be measured in order to achieve a root mean square error (RMSE) of 1.4 dB in path loss.Deep Learning-Based CKM Construction with Image Super-ResolutionShiyu Wang, xiaoli Xu, Yong Zeng, Southeast University