Federated Unsupervised Learning
3 papers with code • 0 benchmarks • 0 datasets
Federated unsupervised learning trains models from decentralized data that have no labels.
Benchmarks
These leaderboards are used to track progress in Federated Unsupervised Learning
Most implemented papers
Collaborative Unsupervised Visual Representation Learning from Decentralized Data
EasyFL-AI/EasyFL
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ICCV 2021
In this framework, each party trains models from unlabeled data independently using contrastive learning with an online network and a target network.
Divergence-aware Federated Self-Supervised Learning
EasyFL-AI/EasyFL
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ICLR 2022
Using the framework, our study uncovers unique insights of FedSSL: 1) stop-gradient operation, previously reported to be essential, is not always necessary in FedSSL; 2) retaining local knowledge of clients in FedSSL is particularly beneficial for non-IID data.
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning
kazkara/adept
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Though there has been a plethora of algorithms proposed for personalized supervised learning, discovering the structure of local data through personalized unsupervised learning is less explored.
