Analysis of Total Variation Minimization for Clustered Federated Learning
A Jung - arXiv preprint arXiv:2403.06298, 2024 - arxiv.org
A key challenge in federated learning applications is the statistical heterogeneity of local
datasets. Clustered federated learning addresses this challenge by identifying clusters of …
datasets. Clustered federated learning addresses this challenge by identifying clusters of …
Clustered federated learning via generalized total variation minimization
Y SarcheshmehPour, Y Tian, L Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
We study optimization methods to train local (or personalized) models for decentralized
collections of local datasets with an intrinsic network structure. This network structure arises …
collections of local datasets with an intrinsic network structure. This network structure arises …
Bayesian optimal change point detection in high-dimensions
We propose the first Bayesian methods for detecting change points in high-dimensional
mean and covariance structures. These methods are constructed using pairwise Bayes …
mean and covariance structures. These methods are constructed using pairwise Bayes …
Bayesian hypothesis testing for equality of high-dimensional means using cluster subspaces
The classical Hotelling's T 2 test and Bayesian hypothesis tests breakdown for the problem
of comparing two high-dimensional population means due to the singularity of the pooled …
of comparing two high-dimensional population means due to the singularity of the pooled …