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.
Chun-Feng Xie, Zhi-Xiong Chen, Wenqiang Yi et al.· 2026 IEEE/CIC International...· 0 citations
The escalating complexity of deep neural networks introduces substantial challenges to deploying federated learning (FL) in resource-limited edge environments. To address these limitations, split federated learning (SFL) has emerged as a promising paradigm, alleviating client-side computational and communication burdens via strategic model splitting, and periodically aggregating client-side and server-side models consistent with the principles of FL. Nevertheless, existing SFL frameworks encounter significant performance degradation arising from data heterogeneity and imbalance, client heterogeneity, as well as constrained wireless resources. To overcome these issues, this paper introduces a novel data distribution deviation-aware split federated learning (DA-SFL) framework. DA-SFL dynamically adjusts aggregation weights according to the deviation of clients’ data distributions from a global distribution, effectively mitigating biases induced by data imbalance and heterogeneity. Furthermore, we theoretically establish the convergence bound of DA-SFL under a non-convex loss function setting, demonstrating that minimizing the data deviation in each training round enhances learning efficacy. Motivated by this, we formulate a mixed-integer nonlinear programming to optimize learning performance under long-term energy constraints. Leveraging the Lyapunov optimization framework, we decompose the problem into a series of tractable subproblems in each learning round, and propose efficient algorithms to find the client scheduling, adaptive cut layer selection, bandwidth allocation, and aggregation weighting policies. Extensive experimental evaluations conducted on Fashion-MNIST, CIFAR-10, and CINIC-10 datasets across diverse scenarios of data heterogeneity and imbalance demonstrate that DA-SFL significantly outperforms baselines regarding test accuracy, time and energy efficiency, while exhibiting notable robustness and scalability.
Chunfeng Xie, Zhixiong Chen, Wenqiang Yi et al.· IEEE Transactions on Communi...· 1 citation
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