Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data

arXiv:2403.16398v1 Announce Type: new
Abstract: 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. However, the performance of existing FUSL methods suffers from insufficient representations, i.e., (1) representation collapse entanglement among local and global models, and (2) inconsistent representation spaces among local models. The former indicates that representation collapse in local model will subsequently impact the global model and other local …

abstract arxiv client cs.ai cs.lg data decentralized decentralized data entanglement federated learning however modeling performance practice representation type unsupervised unsupervised learning