MicroPython Testbed for Federated Learning Algorithms

The original Python Testbed for Federated Learning Algorithms (PTB-FLA) is a light FL framework, which provides generic centralized and decentralized federated learning algorithms, but has the limitation that all the application instances can run only on a single PC. This paper presents the MicroPyton Testbed for Federated Learning Algorithms (MPT-FLA), the new framework that overcomes this limitation such that individual application instances may run on different network nodes like PCs and IoTs, primarily in edge systems. The new framework was validated and evaluated on a wireless LAN of PCs and RPi Pico W boards and wired LAN of PCs, respectively, by using application examples originally developed for the predecessor framework.

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Publication Details

Published
2024-11-26
DOI
https://doi.org/10.1109/telfor63250.2024.10819071
Citations
3
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
1.04
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article

MicroPython Testbed for Federated Learning Algorithms

Ivan Kaštelan, Ilija Bašičević, Miroslav Popović, Miodrag Djukić et al.
3 citations
Privacy-Preserving Technologies in Data
1.04
article

MicroPython Testbed for Federated Learning Algorithms

Ivan Kaštelan, Ilija Bašičević, Miroslav Popović, Miodrag Djukić, Marko Popović, Pavle Vasiljevic
article en
3 citations

Abstract

The original Python Testbed for Federated Learning Algorithms (PTB-FLA) is a light FL framework, which provides generic centralized and decentralized federated learning algorithms, but has the limitation that all the application instances can run only on a single PC. This paper presents the MicroPyton Testbed for Federated Learning Algorithms (MPT-FLA), the new framework that overcomes this limitation such that individual application instances may run on different network nodes like PCs and IoTs, primarily in edge systems. The new framework was validated and evaluated on a wireless LAN of PCs and RPi Pico W boards and wired LAN of PCs, respectively, by using application examples originally developed for the predecessor framework.

University of Novi Sad (RS), RT-RK Institute for Computer Based Systems (Serbia) (RS)
Openalex Percentile: Top 18%
Privacy-Preserving Technologies in Data
1.04
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