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Nanjing University of Posts and Telecommunications

Academic institutionasia · cn
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Research library252linked papers
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Selected work

Representative Papers

FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients

Jun 03, 2024ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services

To address the uneven computational burden imposed by client-side resource heterogeneity in federated learning (FL), this paper proposes FedConv. It trains lightweight submodels directly in compressed convolutional form, eliminating decompression overhead. FedConv introduces the novel “learning-on-model” paradigm—the first approach enabling end-to-end training of compressed submodels. It further designs a transposed-convolution-based expansion mechanism to unify aggregation of heterogeneous submodels while preserving personalized parameters. Complemented by joint optimization on the server using a small public dataset, FedConv achieves an average accuracy improvement of 35.2% across six benchmark datasets, while reducing computational cost by 33.1% and communication cost by 24.8%, significantly outperforming existing FL methods.

7 citationsRead paper

SpectrumFM: A Foundation Model for Intelligent Spectrum Management

May 02, 2025arXiv.org

To address the low recognition accuracy, slow convergence, and poor generalization of existing small-scale models in dynamic spectrum environments, this paper proposes SpectrumFM—a spectral foundation model. Methodologically, SpectrumFM integrates CNNs with multi-head self-attention to enhance IQ-signal representation learning; introduces the first foundation-model paradigm for spectrum analysis, featuring dual self-supervised pretraining tasks—masked signal reconstruction and next-time-step signal prediction; and employs parameter-efficient fine-tuning (e.g., LoRA) for cross-task transfer. Experiments demonstrate significant improvements: 12.1% higher accuracy in automatic modulation classification (AMC), 9.3% gain in wireless technology classification (WTC), an AUC of 0.97 for spectrum sensing at −4 dB SNR, over 10% improvement in anomaly detection performance, faster convergence, and markedly enhanced few-shot adaptation capability.

3 citations1 influentialRead paper

CovertComBench: The First Domain-Specific Testbed for LLMs in Wireless Covert Communication

Jan 26, 2026

This work addresses the lack of suitable benchmarks for evaluating large language models (LLMs) in the domain of wireless covert communication, where stringent security constraints—such as Kullback–Leibler (KL) divergence limits—are critical. To bridge this gap, the authors propose the first LLM-specific evaluation benchmark tailored to this field, encompassing tasks in conceptual understanding, optimization derivation, and code generation. They further introduce a novel automatic scoring mechanism grounded in detection theory, implementing an “LLM-as-Judge” framework. Experimental results reveal that while LLMs achieve strong performance in concept identification (81%) and code generation (83%), their accuracy drops significantly in security-critical mathematical derivations, ranging from 18% to 55%. These findings underscore the models’ limitations in high-order reasoning and affirm their role as assistive tools rather than autonomous solvers in safety-sensitive applications.

1 citationsRead paper
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