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Found 7 papers, 1 papers with code
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning
Communication efficiency is a major challenge in federated learning (FL).
Robust Target Localization in 2D: A Value-at-Risk Approach
To study the VaR formulation, we start by designing a majorizer set that contains any solution of a general percentile problem.
Distributed detection of ARMA signals
While regime (a) spans staple results in large deviation theory, regime (b) is new in distributed detection and is elicited by the ARMA setup.
Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features
Expectation Maximization (EM) is the standard method to learn Gaussian mixtures.
COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization
First-order methods for stochastic optimization have undeniable relevance, in part due to their pivotal role in machine learning.
StochaLM: a Stochastic alternate Linearization Method for distributed optimization
StochaLM can be interpreted as a dual block coordinate ascent method whose block components are selected using the state of an ergodic Markov chain.
DJAM: distributed Jacobi asynchronous method for learning personal models
We prove that DJAM converges with probability one to the solution, provided that the personal loss functions are strongly convex and have Lipschitz gradient.
