Developing Elementary Federated Learning Algorithms Leveraging the ChatGPT

Fuente: arXiv
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Autores principales: Popovic, Miroslav, Popovic, Marko, Kastelan, Ivan, Djukic, Miodrag, Basicevic, Ilija
Formato: Preprint
Publicado: 2023
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author Popovic, Miroslav
Popovic, Marko
Kastelan, Ivan
Djukic, Miodrag
Basicevic, Ilija
author_facet Popovic, Miroslav
Popovic, Marko
Kastelan, Ivan
Djukic, Miodrag
Basicevic, Ilija
contents The Python Testbed for Federated Learning Algorithms is a simple Python FL framework easy to use by ML&AI developers who do not need to be professional programmers, and this paper shows that it is also amenable to emerging AI tools. In this paper, we successfully developed three elementary FL algorithms using the following three steps process: (i) specify context, (ii) ask ChatGPT to complete server and clients' callback functions, and (iii) verify the generated code.
format Preprint
id arxiv_https___arxiv_org_abs_2312_04412
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Developing Elementary Federated Learning Algorithms Leveraging the ChatGPT
Popovic, Miroslav
Popovic, Marko
Kastelan, Ivan
Djukic, Miodrag
Basicevic, Ilija
Distributed, Parallel, and Cluster Computing
The Python Testbed for Federated Learning Algorithms is a simple Python FL framework easy to use by ML&AI developers who do not need to be professional programmers, and this paper shows that it is also amenable to emerging AI tools. In this paper, we successfully developed three elementary FL algorithms using the following three steps process: (i) specify context, (ii) ask ChatGPT to complete server and clients' callback functions, and (iii) verify the generated code.
title Developing Elementary Federated Learning Algorithms Leveraging the ChatGPT
topic Distributed, Parallel, and Cluster Computing
url https://arxiv.org/abs/2312.04412