Semantic communication systems in mobile networks necessitate real-time collaborative updates of semantic encoders as a result of node mobility that induces semantic extraction drift. However, diversity within modal transmission content and heterogeneous encoder architectures, along with challenges such as imbalanced training requirements and pseudo-label noise, limit the effectiveness of general collaborative update approaches. In this paper, we propose Fed-MoSeC, a novel federated learning framework for updating cross-modal semantic encoders. Our framework trains only a newly designed graph neural network-based adapter while freezing heterogeneous encoders for various modalities, converting heterogeneous cross-modal updates into a homogeneous aggregation task, and significantly reducing communication overhead. By combining confidence-based filtering with similarity-matrix distillation, the novel integrated Pseudo-label Noise Counteracting Component (PNCC) is designed to be robust to noisy data. The Training Optimization Component (TOC) based on a bi-level Cournot–Stackelberg game theoretical algorithm achieves near-optimal Subgame-Perfect Nash Equilibrium (SPNE) to incentivize across nodes and maximize update nodes’ utilities with different training levels. Experimental results highlight the advantages of Fed-MoSeC over existing potential application algorithms, reducing communication by 95–97% and improving RSUM by 18% at 70% pseudo-label noise.
Yushi Wang, Zheng-Xin Yu, Gu-Han Zheng et al.· IEEE Transactions on Mobile...· 0 citations
Device-free human pose estimation using commodity WiFi signals has emerged as a promising paradigm for pervasive sensing in mobile computing systems. However, existing approaches often suffer from severe performance degradation when deployed across heterogeneous environments due to complex multipath propagation and domain shifts in wireless signals. In this paper, we present a physics-informed WiFi sensing framework for robust 3D human pose estimation under mobile and cross-environment settings. Our approach explicitly models wireless signal propagation characteristics and incorporates multipath-aware attention to capture environment-dependent signal variations. To further improve generalization, we introduce a disentangled representation learning scheme that separates pose-related features from environment-specific factors, enabling effective cross-domain adaptation without requiring extensive retraining. We implement our system using commodity WiFi devices and evaluate it on multiple public benchmarks, including Person-in-WiFi-3D and MM-Fi, as well as real-world deployments across diverse indoor environments. Experimental results demonstrate that our framework significantly improves robustness and generalization performance compared to state-of-the-art methods, particularly under cross-environment scenarios. These results highlight the potential of physics-informed wireless sensing for enabling reliable, scalable, and infrastructure-free human-centric applications in mobile computing systems.
Kaixuan Huang, Yuanbo Chen, Guangjin Pan et al.· 0 citations
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