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Review
. 2023 Aug 23;23(17):7358.
doi: 10.3390/s23177358.

Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey

Muhammad Asad  1 , Saima Shaukat  1 , Dou Hu  1 , Zekun Wang  1 , Ehsan Javanmardi  1 , Jin Nakazato  1 , Manabu Tsukada  1
Affiliations
Review

Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey

Muhammad Asad et al. Sensors (Basel). .

Abstract

This paper explores the potential for communication-efficient federated learning (FL) in modern distributed systems. FL is an emerging distributed machine learning technique that allows for the distributed training of a single machine learning model across multiple geographically distributed clients. This paper surveys the various approaches to communication-efficient FL, including model updates, compression techniques, resource management for the edge and cloud, and client selection. We also review the various optimization techniques associated with communication-efficient FL, such as compression schemes and structured updates. Finally, we highlight the current research challenges and discuss the potential future directions for communication-efficient FL.

Keywords: client selection; communication efficient; federated learning; model compression; resource management; structured updates.

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Conflict of interest statement

The authors declare no conflict of interest regarding the publication of this research article.

Figures

Figure 1
Figure 1
Comparison of FL with conventional centralized machine learning and distributed learning.
Figure 2
Figure 2
An overview of our survey.
Figure 3
Figure 3
Workflow of communication protocol in FL.
Figure 4
Figure 4
Techniques for clients and server resource management in FL.
Figure 5
Figure 5
Process of incentive mechanism in FL.
See this image and copyright information in PMC

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