U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition

• URL: http://arxiv.org/abs/2602.16749v1
• Date: Wed, 18 Feb 2026 05:00:38 GMT
• Title: U-FedTomAtt: Ultra-lightweight Federated Learning with Attention for Tomato Disease Recognition
• Authors: Romiyal George, Sathiyamohan Nishankar, Selvarajah Thuseethan, Chathrie Wimalasooriya, Yakub Sebastian, Roshan G. Ragel, Zhongwei Liang,
• Abstract summary: Federated learning has emerged as a privacy-preserving and efficient approach for deploying intelligent agricultural solutions.<n>We propose U-FedTomAtt, an ultra-lightweight federated learning framework with attention for tomato disease recognition in resource-constrained and distributed environments.
• Score: 2.628739907490327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning has emerged as a privacy-preserving and efficient approach for deploying intelligent agricultural solutions. Accurate edge-based diagnosis across geographically dispersed farms is crucial for recognising tomato diseases in sustainable farming. Traditional centralised training aggregates raw data on a central server, leading to communication overhead, privacy risks and latency. Meanwhile, edge devices require lightweight networks to operate effectively within limited resources. In this paper, we propose U-FedTomAtt, an ultra-lightweight federated learning framework with attention for tomato disease recognition in resource-constrained and distributed environments. The model comprises only 245.34K parameters and 71.41 MFLOPS. First, we propose an ultra-lightweight neural network with dilated bottleneck (DBNeck) modules and a linear transformer to minimise computational and memory overhead. To mitigate potential accuracy loss, a novel local-global residual attention (LoGRA) module is incorporated. Second, we propose the federated dual adaptive weight aggregation (FedDAWA) algorithm that enhances global model accuracy. Third, our framework is validated using three benchmark datasets for tomato diseases under simulated federated settings. Experimental results show that the proposed method achieves 0.9910% and 0.9915% Top-1 accuracy and 0.9923% and 0.9897% F1-scores on SLIF-Tomato and PlantVillage tomato datasets, respectively.

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