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Found 5 papers, 0 papers with code
A Unified Framework for Gradient-based Clustering of Distributed Data
The proposed family, termed Distributed Gradient Clustering (DGC-$\mathcal{F}_\rho$), is parametrized by $\rho \geq 1$, controling the proximity of users' center estimates, with $\mathcal{F}$ determining the clustering loss.
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
We study high-probability convergence guarantees of learning on streaming data in the presence of heavy-tailed noise.
A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments
In the proposed setup, the grouping of users (based on the data distributions they sample), as well as the underlying statistical properties of the distributions, are apriori unknown.
Gradient Based Clustering
We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality with respect to cluster assignments and cluster center positions.
Personalized Federated Learning via Convex Clustering
The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized via a sum-of-norms penalty, weighted by a penalty parameter $\lambda$.
