Aug 2026· 2026 IEEE/CIC International Conference on Communications in China (ICCC)· pp. 1770-1775· 0 citations· 14 references
Abstract
Wireless multimodal federated learning (MFL) is promising for privacy-preserving edge intelligence, but its practical deployment is challenged by modality heterogeneity, client resource heterogeneity, and the high communication and computation cost of multimodal models. This paper proposes a beamforming-aided wireless MFL framework with dynamic neural network pruning, termed mFedDNP. In mFedDNP, each scheduled client trains a pruned unimodal submodel matched to its communication and computing capability, while the server uses modal compensation to handle missing modalities and maintains updates in a storage-efficient manner. We characterize the effect of pruning on communication and computation cost, and formulate a joint design over client scheduling, neural network pruning, and resource block (RB) allocation under perround latency constraints. We further develop a staleness-aware solution that combines adaptive pruning with matching-based RB allocation. Experimental results on CREMA-D and UCI-HAR show that mFedDNP consistently outperforms benchmark methods, achieving up to 4.89 percentage points higher Macro-F1 and 23.52% shorter convergence time.
Federated learning (FL) enables multiple devices to collaboratively train a global model without sharing local data. However, due to limited local computing capability and communication bandwidth, FL suffers from high learning latency, especially when the model size is large. To address these issues, we propose APQ-FL,...
Xiao-Dong Li, Yulong Gao, C. Chiasserini et al.· IEEE Transactions on Cogniti...· 0 citations
This paper addresses the challenges of dynamic resource allocation and scheduling in 6G in-X subnetworks supporting applications with heterogeneous characteristics by proposing a novel framework that combines Multi-Agent Reinforcement Learning (MARL), Federated Learning (FL), and Explainable AI (XAI).
Experimental results on heterogeneous MNIST and CIFAR-10 settings show that QEF-GT-AdamW consistently improves robustness and convergence performance over representative DecL baselines while achieving favorable accuracy-communication trade-offs under limited wireless resources.
Thieu Van Nguyen, T. Nguyen, Ons Aouedi et al.· 0 citations
A federated random walk averaging framework is proposed, which is a variant of federated averaging (FedAvg) that mitigates data heterogeneity by updating models along random walk (RW) paths and aggregating them at the server and reduces the problem to a general form agnostic to task type and model architecture.
Chang-Heng Wang, Xian-Chao Zhang, Zhi-Qing Wei et al.· 0 citations
Over-the-Air Computation (AirComp) Federated Learning (FL) is actively studied as a communication-efficient technique for distributed Artificial Intelligence (AI) model training. To mitigate the impact of wireless channels on the aggregated global model while addressing client energy sustainability, recent efforts have...
Stefanos Voikos, P. Charatsaris, Maria Diamanti et al.· IEEE Transactions on Wireles...· 0 citations
Federated Learning (FL) enables collaborative model training in the Internet of Vehicles (IoV), helping vehicles overcome data and computational limitations while preserving privacy. However, its performance is constrained by both data heterogeneity and system heterogeneity. Additionally, different FL tasks exhibit div...
Xiang-Qing Su, Yan Huo, Zi-Han Chen et al.· IEEE Transactions on Network...· 0 citations
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