FedUSC: Collaborative Unsupervised Representation Learning From Decentralized Data for Internet of Things

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.

Automated summary

In this article, a novel federated unsupervised learning method for image classification is proposed, which can be performed without any ground truth annotations. To address the issue of non-independent and identically distributed (non-IID) decentralized data in IoT scenarios, a dynamic update mechanism based on weights divergence is further proposed. Extensive experiments show that our method outperforms all baseline methods in terms of classification accuracy.