When Decentralized Optimization Meets Federated Learning

Hongchang Gao

doi:10.1109/MNET.132.2200530

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When Decentralized Optimization Meets Federated Learning

2023-09-01

Hongchang Gao *

My T. Thai

Xiangyu Wu

DOI：10.1109/MNET.132.2200530

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Abstract

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Federated learning is a new learning paradigm for extracting knowledge from distributed data. Due to its favorable properties in preserving privacy and saving communication costs, it has been extensively studied and widely applied to numerous data analysis applications. However, most existing federated learning approaches concentrate on the centralized setting, which is vulnerable to a single-point failure. An alternative strategy for addressing this issue is the decentralized communication topology. In this article, we systematically investigate the challenges and opportunities when renovating decentralized optimization for federated learning. In particular, we discussed them from the model, data, and communication sides, respectively, which can deepen our understanding about decentralized federated learning.

Keywords:

Optimization

Data models

Computational modeling

Adaptation models

Servers

Convergence

Stochastic processes

Federated learning

Distributed databases

Information retrieval

Knowledge acquisition