To address the big data challenges, serverless multi-party collaborative training has recently
attracted attention in the data mining community, since they can cut down the …

We analyze the sample complexity of single-loop quadratic penalty and augmented
Lagrangian algorithms for solving nonconvex optimization problems with functional equality …

We first propose a decentralized proximal stochastic gradient tracking method (DProxSGT)
for nonconvex stochastic composite problems, with data heterogeneously distributed on …

In recent years, decentralized bilevel optimization problems have received increasing
attention in the networking and machine learning communities. However, for decentralized …

We propose a novel single-loop decentralized algorithm, DGDA-VR, for solving the
stochastic nonconvex strongly-concave minimax problems over a connected network of …

Stochastic optimization (SO) algorithms based on the Powerball function, namely powered
stochastic optimization (PoweredSO) algorithms, have been confirmed, effectively, and …

This paper proposes the Doubly Compressed Momentum-assisted stochastic gradient
tracking algorithm $\texttt {DoCoM} $ for communication-efficient decentralized optimization …

The stochastic compositional minimax problem has attracted a surge of attention in recent
years since it covers many emerging machine learning models. Meanwhile, due to the …

Decentralized learning recently has received increasing attention in machine learning due
to its advantages in implementation simplicity and system robustness, data privacy …

The rapid advances in federated learning (FL) in the past few years have recently inspired a
great deal of research on this emerging topic. Existing work on FL often assume that clients …