不完美数据感知下的分布式边缘学习
Distributed Edge Learning under Imperfect Data Sensing
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中文总结 AI 辅助
研究不完美数据感知下的分布式边缘学习，将传感噪声建模为结构化协方差，推导非凸学习收敛界，得出相关可达界和阈值，联合优化模态等参数，通过仿真展示性能增益。
中文摘要 AI 辅助
分布式学习系统通常假定客户端已有固定质量的本地数据，而实际中数据通过不完美物理过程感知，其质量取决于模态、分辨率、传感能力和样本大小。我们将传感噪声建模为结构化、模态相关协方差，推导非凸学习收敛界，其不可约传感底限由模态噪声协方差与损失敏感性几何结构的对齐决定。最优模态最小化此噪声梯度对齐而非仅总噪声功率。分析还得出了ε平稳性的传感器硬件可达界和累积数据集大小的硬件饱和阈值。我们联合优化模态、分辨率、功率和样本数，并通过仿真展示性能提升。
英文摘要
Distributed learning systems typically assume that local data is already available at clients with fixed quality, while in practice, data is sensed through imperfect physical processes whose quality depends on modality, resolution, sensing power, and sample size. We model sensing noise as a structured, modality-dependent covariance and derive a non-convex learning convergence bound whose irreducible sensing floor is governed by the alignment between the modality noise covariance and the loss-sensitivity geometry. Thus, the optimal modality minimizes this noise-gradient alignment rather than total noise power alone. The analysis further yields a sensor-hardware achievability bound for epsilon-stationarity and a hardware-saturation threshold on the accumulated dataset size. We jointly optimize modality, resolution, power, and sample count and demonstrate the performance gain through simulations.
