提前估算边缘感知系统的训练难度，帮开发者判断资源够不够用。
Pre-Deployment Complexity Estimation for Federated Perception Systems
- 用数据本身的属性和客户端分布来预估联邦学习的复杂度。
- 复杂度越高，准确率越低，通信开销越大，结果稳定可靠。
- 适合边缘AI部署前做可行性评估和资源规划的人看。
边缘AI系统越来越多依赖联邦学习，在分布式、隐私保护且资源受限的环境下训练感知模型。然而，训练前从业者往往缺乏实用工具来评估任务难度，包括预期准确率和通信成本。本文提出一种与分类器无关的预部署框架，结合数据的内在特性（如维度、稀疏性、异质性）与客户端分布组成，估算联邦感知系统的学习复杂度。以联邦学习为例，研究不同配置下学习难度的变化。在三个MNIST变体上的实验表明，综合复杂度指标与最高及平均联邦准确率呈强负相关，而内在与分布式组件分别与通信开销保持一致关系。结果表明，复杂度估计可作为边缘部署感知系统中资源规划、数据评估和可行性判断的实用诊断工具。
原文摘要 · Abstract (English)
Edge AI systems increasingly rely on federated learning to train perception models in distributed, privacy-preserving, and resource-constrained environments. Before training, however, practitioners often lack practical tools for estimating task difficulty in terms of expected accuracy and communication effort. We present a classifier-agnostic, pre-deployment framework that combines intrinsic data properties such as dimensionality, sparsity, and heterogeneity, with client-distribution composition to estimate learning complexity in federated perception systems. Using federated learning as a representative distributed training setting, we examine how learning difficulty varies across different federated configurations. Experiments on three MNIST variants show strong negative correlations between the combined complexity metric and maximum and average federated accuracy, while the intrinsic and distributed components exhibit consistent relationships with communication effort. These findings suggest that complexity estimation can serve as a practical diagnostic tool for resource planning, dataset assessment, and feasibility evaluation in edge-deployed perception systems.
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