FedEdge: Federated Learning with Docker and Kubernetes forScalable and Efficient Edge Computing
Authors: 
Mir Hassan, Leonardo Lucio Custode, 
Kasim Sinan Yildirim, Giovanni IaccaAuthors Info & Claims
Mir Hassan, Leonardo Lucio Custode, Kasim Sinan Yildirim, Giovanni IaccaAuthors Info & ClaimsEWSN '23: Proceedings of the 2023 INTERNATIONAL CONFERENCE ON EMBEDDED WIRELESS SYSTEMS AND NETWORKS
Pages 339 - 344
Abstract
This paper introduces a novel framework that integrates Docker and Kubernetes to address well-known challenges in federated learning. Federated learning has gained significant attention as a privacy-preserving and scalable machine learning paradigm. However, existing frameworks often lack portability, scalability, resource efficiency, fault tolerance, standardization, and ecosystem integration. To overcome these limitations, we propose a conceptual framework that combines Tensorflow FL with Docker’s containerization capabilities and Kubernetes’ orchestration capabilities. Our approach fills all the gaps in existing FL frameworks. By leveraging Docker containers, our model achieves efficient resource allocation, maximizing computing resources while maintaining portability and scalability. Kubernetes further enhances resource allocation by orchestrating the deployment of these containers, minimizing resource consumption. Our proposed framework provides opportunities for large-scale distributed machine learning applications, enabling the widespread use of federated learning methodologies.
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Information & Contributors
Information
Published In
December 2023
426 pages
Sponsors
In-Cooperation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 15 December 2023
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EWSN '23
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EWSN '23 Paper Acceptance Rate 31 of 56 submissions, 55%;
Overall Acceptance Rate 81 of 195 submissions, 42%
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