AI Chat AI Image Generator AI Video Text to Speech

Federated Learning via Inexact ADMM

04/22/2022

∙

by Shenglong Zhou, et al.

∙

∙

One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full devices participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based algorithms, this paper develops an inexact alternating direction method of multipliers (ADMM), which is both computation and communication-efficient, capable of combating the stragglers' effect, and convergent under mild conditions.

Shenglong Zhou
14 publications
Geoffrey Ye Li
91 publications

research

∙ 05/03/2022

Efficient and Convergent Federated Learning

Federated learning has shown its advances over the last few years but is...

0 Shenglong Zhou, et al. ∙

research

∙ 10/18/2021

Towards Federated Bayesian Network Structure Learning with Continuous Optimization

Traditionally, Bayesian network structure learning is often carried out ...

1 Ignavier Ng, et al. ∙

research

∙ 10/14/2017

Robust Federated Learning Using ADMM in the Presence of Data Falsifying Byzantines

In this paper, we consider the problem of federated (or decentralized) l...

0 Qunwei Li, et al. ∙

research

∙ 10/28/2021

Communication-Efficient ADMM-based Federated Learning

Federated learning has shown its advances over the last few years but is...

0 Shenglong Zhou, et al. ∙

research

∙ 09/05/2020

Distributed Optimization, Averaging via ADMM, and Network Topology

There has been an increasing necessity for scalable optimization methods...

11 Guilherme França, et al. ∙

research

∙ 06/17/2022

FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning

Newton-type methods are popular in federated learning due to their fast ...

14 Anis Elgabli, et al. ∙

research

∙ 07/07/2019

Fast and Provable ADMM for Learning with Generative Priors

In this work, we propose a (linearized) Alternating Direction Method-of-...

0 Fabian Latorre Gómez, et al. ∙