Edge AI systems increasingly rely on federated learning to train perception models in distributed, privacy-preserving, and resource-constrained environments. Yet, before training begins, practitioners often lack practical tools to estimate how difficult a federated learning task will be in terms of achievable accuracy and communication cost. This paper presents a classifier-agnostic, pre-deployment framework for estimating learning complexity in federated perception systems by jointly modeling intrinsic properties of the data and characteristics of the distributed environment. The proposed complexity metric integrates dataset attributes such as dimensionality, sparsity, and heterogeneity with factors related to the composition of participating clients. Using federated learning as a representative distributed training setting, we examine how learning difficulty varies across different federated configurations. Experiments on multiple variants of the MNIST dataset and CIFAR dataset show that the proposed metric strongly correlates with federated learning performance and the communication effort required to reach fixed accuracy targets. 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.

翻译：边缘人工智能系统日益依赖联邦学习在分布式、隐私保护和资源受限的环境中训练感知模型。然而，在训练开始前，实践者往往缺乏实用工具来评估联邦学习任务在可达精度和通信开销方面的难度。本文提出了一种与分类器无关的部署前框架，通过联合建模数据内在属性与分布式环境特征，来估计联邦感知系统中的学习复杂度。所提出的复杂度度量整合了数据集维度、稀疏性和异质性等属性，并结合了参与客户端的组成特征。以联邦学习作为代表性分布式训练场景，我们考察了不同联邦配置下学习难度的变化规律。在MNIST数据集和CIFAR数据集的多种变体上进行的实验表明，该度量与联邦学习性能以及达到固定精度目标所需的通信开销呈强相关性。这些发现表明，复杂度估计可作为边缘部署感知系统在资源规划、数据集评估和可行性验证中的实用诊断工具。