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

Romiyal George, Sathiyamohan Nishankar, Selvarajah Thuseethan, Chathrie Wimalasooriya, Yakub Sebastian, Roshan G. Ragel, Zhongwei Liang

TL;DR

This paper proposes U-FedTomAtt, an ultra-lightweight federated learning framework with attention for tomato disease recognition in resource-constrained and distributed environments, which comprises only 245.34K parameters and 71.41 MFLOPS.

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.

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

TL;DR

This paper proposes U-FedTomAtt, an ultra-lightweight federated learning framework with attention for tomato disease recognition in resource-constrained and distributed environments, which comprises only 245.34K parameters and 71.41 MFLOPS.

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.
Paper Structure (19 sections, 6 equations, 4 figures, 4 tables)

This paper contains 19 sections, 6 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Attention-aware Lightweight Neural Networks
  4. FL Approaches
  5. Proposed Method
  6. Problem Setting
  7. FedDAWA
  8. Data Size Component ($P_k$)
  9. Inverse Validation Loss Component ($Q_k$)
  10. Adaptive Aggregation Formulation
  11. Global Aggregation
  12. Ultra-lightweight Network
  13. DBNeck
  14. LoGRA
  15. Experiments
  16. ...and 4 more sections

Figures (4)

  • Figure 1: Illustration of the proposed ultra-lightweight neural network with LoGRA for tomato leaf disease recognition. The red arrows denote residual connections, the blue arrows represent standard connections, and the green dashed lines indicate the expansion of the layer architecture.
  • Figure 2: Illustration of FedDAWA algorithm.
  • Figure 3: The selection of the hyperparameter $\beta$ for SLIF-Tomato and PlantVillage tomato datasets.
  • Figure 4: Evolution of local and global model accuracies over 50 communication rounds. The continuous lines and broken lines indicate the accuracy of the global model and local models, respectively.