Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets
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Date
2023-09
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Abstract
Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising
from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages
technologies for enhanced crop production, with disease detection being a vital element. Prompt
identification of diseases in paddy leaves is critical for curtailing their propagation and reducing
crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas
and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep
learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited
for widespread application. These ML/DL methods cannot ensure data privacy, as they involve
sharing training data with a central server, overlooking competitive and regulatory considerations.
As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified
limitations of centralized training. This paper utilizes the FL approach for the classification of
rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification
with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of
rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial
blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called
federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized
client-server setup. Both IID (independent and identically distributed) and non-IID datasets were
utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness
analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them,
MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently,
we trained these models using both IID and non-IID datasets in a federated learning environment.
The framework’s performance was assessed through diverse scenarios, comparing it with traditional
and federated learning models. The evaluation considered metrics like validation accuracy, loss as
well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving
99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower
training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation,
EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved
90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the
F-TL framework’s superiority over traditional distributed deep-learning classifiers, demonstrating its
effectiveness in both single and multiclient instances. Notably, the framework’s strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as
a valuable alternative for rice-leaf disease classification compared to existing tools.