Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification
Early detection of plant diseases is critical for improving crop productivity, while it also facilitates the foundations of precision agriculture. Recent advances in distributed deep learning have enabled plant disease classification models to be trained across geographically distributed agricultural sensing infrastructures. However, deploying such systems in large-scale Internet of Things (IoT) environments, introduces significant challenges related to computational cost, energy consumption, and system efficiency. In this paper, we present a design-space exploration of hierarchical federated learning architectures for plant disease classification, with a particular focus on the trade-off...
| Authors | Athanasios Papanikolaou, Athanasios Tziouvaras, Pavlos Stoikos et al. |
|---|---|
| Primary category | cs.DC |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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