Papers Federated Unsupervised Learning
“Federated Unsupervised Learning” 태그가 달린 논문 5편 · 필터 해제
A Mutual Information Perspective on Federated Contrastive Learning
We investigate contrastive learning in the federated setting through the lens of SimCLR and multi-view mutual information maximization. In doing so, we uncover a connection between contrastive representation learning and…
Contrastive LearningFederated Unsupervised LearningRepresentation LearningRethinking the Representation in Federated Unsupervised Learning with Non-IID Data
Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised learning (FUSL) with non-IID data. Howe…
Federated LearningFederated Unsupervised LearningADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
Statistical heterogeneity of clients' local data is an important characteristic in federated learning, motivating personalized algorithms tailored to the local data statistics. Though there has been a plethora of algorit…
Dimensionality ReductionFederated LearningFederated Unsupervised LearningDivergence-aware Federated Self-Supervised Learning
Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlab…
Federated LearningFederated Unsupervised LearningLinear evaluationSelf-Supervised LearningCollaborative Unsupervised Visual Representation Learning from Decentralized Data
Unsupervised representation learning has achieved outstanding performances using centralized data available on the Internet. However, the increasing awareness of privacy protection limits sharing of decentralized unlabel…
Contrastive LearningFederated LearningFederated Unsupervised LearningRepresentation Learning+1