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ns3-fl: Simulating Federated Learning with ns-3

Authors:

Muhammad Huzaifa,

Tajana RosingAuthors Info & Claims

WNS3 '22: Proceedings of the 2022 Workshop on ns-3

Pages 97 - 104

https://doi.org/10.1145/3532577.3532591

Published: 22 June 2022 Publication History

Abstract

In recent years, there has been a spike in interest in the field of federated learning (FL). As a result, an increasing number of federated learning algorithms have been developed. Large-scale deployments to validate these algorithms are often not feasible, resulting in a need for simulation tools which closely emulate real deployment conditions. Existing federated learning simulators lack complex network settings, and instead focus on data and algorithmic development. ns-3 is a discrete event network simulator, which has a plethora of models to represent network components and can simulate complex networking scenarios. In this paper, we present ns3-fl, which is a tool that connects an existing FL simulator, flsim, with ns-3 to produce a federated learning simulator that considers data, algorithm, and network. We first discuss the learning, network and power models used to develop our tool. We then present an overview of our implementation, including the Client/Server ns-3 applications and interprocess communication protocols. A real Raspberry Pi-based deployment is setup to validate our tool. Finally, we perform a simulation emulating FL training on 40 clients throughout the UCSD campus and analyze the performance of our tool, in terms of real clock execution time for various FL rounds.

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Cited By

Elshair IKhanzada TShahid MSiddiqui S(2024)Evaluating Federated Learning Simulators: A Comparative Analysis of Horizontal and Vertical ApproachesSensors10.3390/s2416514924:16(5149)Online publication date: 9-Aug-2024
https://doi.org/10.3390/s24165149
Mwase CJin YWesterlund TTenhunen HZou Z(2024)DAI-NET: Toward communication-aware collaborative training for the industrial edgeFuture Generation Computer Systems10.1016/j.future.2024.01.027155(193-203)Online publication date: Jun-2024
https://doi.org/10.1016/j.future.2024.01.027
Farhad APyun J(2023)LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine LearningSensors10.3390/s2315685123:15(6851)Online publication date: 1-Aug-2023
https://doi.org/10.3390/s23156851

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cover image ACM Other conferences

WNS3 '22: Proceedings of the 2022 Workshop on ns-3

June 2022

134 pages

ISBN:9781450396516

DOI:10.1145/3532577

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2022

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WNS3 2022

WNS3 2022: 2022 Workshop on ns-3

June 22 - 23, 2022

Virtual Event, USA

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Cited By

Elshair IKhanzada TShahid MSiddiqui S(2024)Evaluating Federated Learning Simulators: A Comparative Analysis of Horizontal and Vertical ApproachesSensors10.3390/s2416514924:16(5149)Online publication date: 9-Aug-2024
https://doi.org/10.3390/s24165149
Mwase CJin YWesterlund TTenhunen HZou Z(2024)DAI-NET: Toward communication-aware collaborative training for the industrial edgeFuture Generation Computer Systems10.1016/j.future.2024.01.027155(193-203)Online publication date: Jun-2024
https://doi.org/10.1016/j.future.2024.01.027
Farhad APyun J(2023)LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine LearningSensors10.3390/s2315685123:15(6851)Online publication date: 1-Aug-2023
https://doi.org/10.3390/s23156851
Farhad APyun J(2023)Terahertz Meets AI: The State of the ArtSensors10.3390/s2311503423:11(5034)Online publication date: 24-May-2023
https://doi.org/10.3390/s23115034

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