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• W4361761348 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 article, 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 nonindependent and identically distributed (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 data sets and outperform several federated unsupervised learning methods under non-IID settings." @default.
• W4361761348 created "2023-04-04" @default.
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• W4361761348 date "2023-08-01" @default.
• W4361761348 modified "2023-09-25" @default.
• W4361761348 title "FedUSC: Collaborative Unsupervised Representation Learning From Decentralized Data for Internet of Things" @default.
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• W4361761348 doi "https://doi.org/10.1109/jiot.2023.3262669" @default.
• W4361761348 hasPublicationYear "2023" @default.
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