Constrained Bayesian Optimization for Hierarchical Federated Learning in IoT Networks for Plant Disease Classification

2026-09-06

AI Summary

本文提出了一种约束贝叶斯优化框架，用于在资源受限的物联网环境中高效配置分层联邦学习，以平衡能耗、执行时间和预测性能。

Abstract

The deployment of Hierarchical Federated Learning (HFL) in resource-constrained Internet of Things (IoT) environments requires careful configuration to balance predictive performance with energy consumption and execution time. This challenge is particularly relevant to smart agriculture, where distributed IoT devices can support automated plant disease classification while operating under limited computational and communication resources. This paper presents a constrained Bayesian Optimization framework for the efficient configuration of HFL deployments. The proposed approach jointly explores the deep learning backbone architecture, aggregation strategy, and number of communication rounds, while the federation size is determined according to the spatial coverage requirements of the agricultural deployment. A weighted objective function captures user-defined trade-offs among energy consumption, execution time, and predictive performance, while explicit constraints ensure compliance with deployment-specific resource and accuracy requirements. The framework is evaluated on an IoT-based plant disease classification task considering multiple deep learning architectures, federated aggregation strategies, and communication-round settings. Experimental results across 30 independent optimization runs show that the proposed approach explores only 11.11% of the search space, while consistently identifying solutions within 1% of the exhaustive-search optimum, with a mean optimality gap of only 0.056%.

Problem

• Hierarchical Federated Learning
• IoT Networks
• Plant Disease Classification
• Resource Constraints
• Energy Consumption

Innovation

• Constrained Bayesian Optimization
• Hierarchical Federated Learning
• IoT Networks
• Plant Disease Classification
• Resource-constrained