Pre-Deployment Complexity Estimation for Federated Perception Systems
This provides a practical diagnostic tool for resource planning and feasibility evaluation in edge-deployed perception systems, though it is incremental as it builds on existing federated learning methods.
The paper tackles the problem of estimating learning difficulty in federated perception systems before deployment, proposing a framework that integrates data and environment characteristics, and shows the metric correlates strongly with performance and communication costs in experiments on MNIST and CIFAR datasets.
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.