Toward Privacy-Preserving Waste Classification in the Internet of Things

Diffuse waste data and associated privacy concerns present significant challenges for effective waste classification in the Internet of Things (IoT) realm. This research introduces a novel approach that leverages differential privacy (DP) and federated transfer learning (FTL) to address the issues,...

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Bibliographic Details
Published in:IEEE internet of things journal 2024-07, Vol.11 (14), p.24814-24830
Main Authors: Quan, Minh K., Nguyen, Dinh C., Nguyen, Van-Dinh, Wijayasundara, Mayuri, Setunge, Sujeeva, Pathirana, Pubudu N.
Format: Article
Language:English
Subjects:
Classification
Computational modeling
Data models
Data privacy
Datasets
Effectiveness
Federated learning
Federated learning (FL)
Garbage
Internet of Things
Internet of Things (IoT)
Privacy
privacy preservation
Standards organizations
Training
transfer learning (TL)
waste classification
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Summary:Diffuse waste data and associated privacy concerns present significant challenges for effective waste classification in the Internet of Things (IoT) realm. This research introduces a novel approach that leverages differential privacy (DP) and federated transfer learning (FTL) to address the issues, enabling waste classification while preserving privacy within the IoT ecosystem. By integrating federated learning (FL), transfer learning (TL), and DP, our proposed method facilitates collaborative training while ensuring data privacy. In this methodology, a pretrained model, initially trained on the ImageNet data set, is disseminated to IoT devices. Subsequently, these devices perform local training using the TrashNet and Garbage Classification data sets. This process allows devices to capture waste characteristics unique to their individual environments. Through the fusion of general knowledge pertaining to the trained model and local insights, the proposed approach achieves efficient waste classification. The study critically examines the implications for privacy, biases resulting from limited local data, and tradeoffs between privacy and model performance. The experimental evaluation demonstrates the effectiveness of the approach and underscores the importance of ensuring privacy-sensitive waste classification. This research contributes to the discourse on FTL and encourages further research into privacy-preserving waste classification within the IoT.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3386727