In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

We study a variation of vanilla stochastic gradient descent where the optimizer only has
access to a Markovian sampling scheme. These schemes encompass applications that …

There are two categories of methods in Federated Learning (FL) for joint training across
multiple clients: i) parallel FL (PFL), where clients train models in a parallel manner; and ii) …

This paper delves into stochastic optimization problems that involve Markovian noise. We
present a unified approach for the theoretical analysis of first-order gradient methods for …

Analyzing data owned by several parties while achieving a good trade-off between utility
and privacy is a key challenge in federated learning and analytics. In this work, we introduce …

Recently there is a large amount of work devoted to the study of Markov chain stochastic
gradient methods (MC-SGMs) which mainly focus on their convergence analysis for solving …

The increase in the computational power of edge devices has opened up the possibility of
processing some of the data at the edge and distributing model learning. This paradigm is …

In this paper, we study the adaptive step size random walk gradient descent with momentum
for decentralized optimization, in which the training samples are drawn dependently with …

To handle the data explosion in the era of Internet-of-things, it is of interest to investigate the
decentralized network, with the aim at relaxing the burden at the central server along with …

We study differentially private (DP) machine learning algorithms as instances of noisy fixed-
point iterations, in order to derive privacy and utility results from this well-studied framework …