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Found 6 papers, 1 papers with code
Analysis of Total Variation Minimization for Clustered Federated Learning
A key challenge in federated learning applications is the statistical heterogeneity of local datasets.
Towards Model-Agnostic Federated Learning over Networks
Our method is an instance of empirical risk minimization, with the regularization term derived from the network structure of data.
flow-based clustering and spectral clustering: a comparison
What sets our approach apart from spectral clustering is that we do not use the eigenvectors of a graph Laplacian to construct the feature vectors.
Federated Learning From Big Data Over Networks
We obtain a distributed federated learning algorithm via a message passing implementation of this primal-dual method.
Explainable Empirical Risk Minimization
One user might have a university degree in machine learning or related fields, while another user might have never received formal training in high-school mathematics.
Analysis of Network Lasso for Semi-Supervised Regression
This approach lends quite naturally to highly scalable learning algorithms in the form of message passing over an empirical graph which represents the network structure of the data.
