Original scientific paper
https://doi.org/10.32985/ijeces.17.5.3
A Comparative Study of Federated and Centralised Learning for Waste Classification with Non-IID Data
Qian Wang
; Universiti Teknologi MARA, Faculty of Computer and Mathematical Sciences Shah Alam, Selangor, Malaysia
*
Lei Wang
orcid.org/0009-0008-1948-1007
; Universiti Teknologi MARA, Faculty of Computer and Mathematical Sciences Shah Alam, Selangor, Malaysia
Shafaf Ibrahim
; Universiti Teknologi MARA, Faculty of Computer and Mathematical Sciences Shah Alam, Selangor, Malaysia
Zainura Idrus
; Universiti Teknologi MARA, Faculty of Computer and Mathematical Sciences Shah Alam, Selangor, Malaysia
* Corresponding author.
Abstract
Accurate waste classification is essential for sustainable environmental management, as traditional manual approaches are time-consuming, labour-intensive, and prone to human error. Deep Learning (DL) has achieved remarkable progress in image- based classification, but its dependence on large, labelled datasets and centralised training raises concerns about data privacy and scalability. Federated Learning (FL) provides a privacy-preserving alternative by enabling model training across decentralised devices without sharing raw data. However, applying FL to waste image classification remains challenging due to the non-independent and identically distributed (non-IID) nature of client data, caused by variations in environment, lighting, and user habits.To address this, we propose a privacy-preserving and adaptive FL framework tailored for waste image classification under heterogeneous data distributions. Five Convolutional Neural Network (CNN) architectures—ResNet-18, ResNet-50, GoogLeNet, DenseNet-121, and VGG- 19—were systematically compared under both centralised DL and FL settings. Experimental results show that GoogLeNet achieves the highest accuracy, reaching 80.45% under non-IID FL conditions, outperforming centralised DL by up to 9.8% in specific configurations. These findings demonstrate the effectiveness of FL in improving generalisation and robustness while preserving privacy, providing practical insights for developing scalable, intelligent waste management systems in real-world, diverse environments.
Keywords
Federated Learning (FL); non-IID data; Deep Learning (DL); Convolutional Neural Networks (CNNs); waste classification; adaptive aggregation; data heterogeneity; privacy-preserving intelligence; smart waste management;
Hrčak ID:
346859
URI
Publication date:
4.5.2026.
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