Federated Latent Class Regression for Hierarchical Data. (arXiv:2206.10783v1 [cs.LG])

Web: http://arxiv.org/abs/2206.10783

Federated Learning (FL) allows a number of agents to participate in training
a global machine learning model without disclosing locally stored data.
Compared to traditional distributed learning, the heterogeneity (non-IID) of
the agents slows down the convergence in FL. Furthermore, many datasets, being
too noisy or too small, are easily overfitted by complex models, such as deep
neural networks. Here, we consider the problem of using FL regression on noisy,
hierarchical and tabular datasets in which user distributions are significantly …

arxiv data hierarchical lg regression