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

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...

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Detalles Bibliográficos
Autores principales: Asad, Muhammad, Shaukat, Saima, Hu, Dou, Wang, Zekun, Javanmardi, Ehsan, Nakazato, Jin, Tsukada, Manabu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490700/
https://www.ncbi.nlm.nih.gov/pubmed/37687814
http://dx.doi.org/10.3390/s23177358
Descripción
Sumario: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.