FedUSC: Collaborative Unsupervised Representation Learning from Decentralized Data for Internet of Things
IEEE Internet of Things Journal
; : 1-1, 2023.
Article
in English
| Scopus | ID: covidwho-2306501
ABSTRACT
Federated Learning (FL) lately has shown much promise in improving the shared model and preserving data privacy. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. In this paper, we propose a novel federated unsupervised learning method for image classification without the use of any ground truth annotations. In IoT scenarios, a big challenge is that decentralized data among multiple clients is normally non-IID, leading to performance degradation. To address this issue, we further propose a dynamic update mechanism that can decide how to update the local model based on weights divergence. Extensive experiments show that our method outperforms all baseline methods by large margins, including +6.67% on CIFAR-10, +5.15% on STL-10, and +8.44% on SVHN in terms of classification accuracy. In particular, we obtain promising results on Mini-ImageNet and COVID-19 datasets and outperform several federated unsupervised learning methods under non-IID settings. IEEE
Contrastive learning; Data models; Federated learning; Internet of Things; Internet of Things (IoT); Semantics; Task analysis; Training; Unsupervised learning; Job analysis; Privacy-preserving techniques; Decentralised; High quality; Internet of thing; Labeled data; Shared model; Unsupervised learning method
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Internet of Things Journal
Year:
2023
Document Type:
Article
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