System Optimization in Synchronous Federated Training: A Survey

09/09/2021
by   Zhifeng Jiang, et al.
0

The unprecedented demand for collaborative machine learning in a privacy-preserving manner gives rise to a novel machine learning paradigm called federated learning (FL). Given a sufficient level of privacy guarantees, the practicality of an FL system mainly depends on its time-to-accuracy performance during the training process. Despite bearing some resemblance with traditional distributed training, FL has four distinct challenges that complicate the optimization towards shorter time-to-accuracy: information deficiency, coupling for contrasting factors, client heterogeneity, and huge configuration space. Motivated by the need for inspiring related research, in this paper we survey highly relevant attempts in the FL literature and organize them by the related training phases in the standard workflow: selection, configuration, and reporting. We also review exploratory work including measurement studies and benchmarking tools to friendly support FL developers. Although a few survey articles on FL already exist, our work differs from them in terms of the focus, classification, and implications.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/08/2022

A Survey on Participant Selection for Federated Learning in Mobile Networks

Federated Learning (FL) is an efficient distributed machine learning par...
research
06/12/2020

Heterogeneity-Aware Federated Learning

Federated learning (FL) is an emerging distributed machine learning para...
research
04/26/2023

Bayesian Federated Learning: A Survey

Federated learning (FL) demonstrates its advantages in integrating distr...
research
08/23/2023

A Survey for Federated Learning Evaluations: Goals and Measures

Evaluation is a systematic approach to assessing how well a system achie...
research
01/23/2022

A Comprehensive Survey on Federated Learning: Concept and Applications

This paper provides a comprehensive study of Federated Learning (FL) wit...
research
07/05/2023

Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives

Traditional Federated Learning (FL) follows a server-domincated cooperat...
research
04/14/2023

Federated and distributed learning applications for electronic health records and structured medical data: A scoping review

Federated learning (FL) has gained popularity in clinical research in re...

Please sign up or login with your details

Forgot password? Click here to reset