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Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey.
- Source :
-
Sensors (14248220) . Sep2023, Vol. 23 Issue 17, p7358. 31p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*MATHEMATICAL optimization
*RESOURCE management
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 23
- Issue :
- 17
- Database :
- Academic Search Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 171855656
- Full Text :
- https://doi.org/10.3390/s23177358