Elastic Federated Learning with Kubernetes Vertical Pod Autoscaler for edge computing

Pham, Khanh Quan; Kim, Taehong

doi:10.1016/j.future.2024.04.047

Future Generation Computer Systems

2024

DOI: 10.1016/j.future.2024.04.047

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Elastic Federated Learning with Kubernetes Vertical Pod Autoscaler for edge computing

Khanh Quan Pham,

Taehong Kim

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Cited by 2 publications

References 22 publications

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Special Issue on Intelligent Architectures and Platforms for Private Edge Cloud Systems

Shah,

Kim,

Varghese

et al. 2025

Future Generation Computer Systems

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Special Issue on Intelligent Architectures and Platforms for Private Edge Cloud Systems

Shah,

Kim,

Varghese

et al. 2025

Future Generation Computer Systems

View full text Add to dashboard Cite

Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems

Liberti,

Berardi,

Martini

2024

Applied Sciences

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Federated Learning (FL) represents a promising distributed learning methodology particularly suitable for dynamic and heterogeneous environments characterized by the presence of Internet of Things (IoT) devices and Edge Computing infrastructures. In this context, FL allows you to train machine learning models directly on edge devices, mitigating data privacy concerns and reducing latency due to transmitting data to central servers. However, the heterogeneity of computational resources, the variability of network connections, and the mobility of IoT devices pose significant challenges to the efficient implementation of FL. This work explores advanced techniques for dynamic model adaptation and heterogeneous data management in edge computing scenarios, proposing innovative solutions to improve the robustness and efficiency of federated learning. We present an innovative solution based on Kubernetes which enables the fast application of FL models to Heterogeneous Architectures. Experimental results demonstrate that our proposals can improve the performance of FL in IoT and edge environments, offering new perspectives for the practical implementation of decentralized intelligent systems.

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Elastic Federated Learning with Kubernetes Vertical Pod Autoscaler for edge computing

Cited by 2 publications

References 22 publications

Special Issue on Intelligent Architectures and Platforms for Private Edge Cloud Systems

Special Issue on Intelligent Architectures and Platforms for Private Edge Cloud Systems

Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems

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