fluke: Federated Learning Utility frameworK for Experimentation and research

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Xuất bản năm:arXiv.org (Dec 20, 2024), p. n/a
Tác giả chính: Polato, Mirko
Được phát hành:
Cornell University Library, arXiv.org
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MARC

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022 |a 2331-8422 
035 |a 3148683152 
045 0 |b d20241220 
100 1 |a Polato, Mirko 
245 1 |a fluke: Federated Learning Utility frameworK for Experimentation and research 
260 |b Cornell University Library, arXiv.org  |c Dec 20, 2024 
513 |a Working Paper 
520 3 |a Since its inception in 2016, Federated Learning (FL) has been gaining tremendous popularity in the machine learning community. Several frameworks have been proposed to facilitate the development of FL algorithms, but researchers often resort to implementing their algorithms from scratch, including all baselines and experiments. This is because existing frameworks are not flexible enough to support their needs or the learning curve to extend them is too steep. In this paper, we present \fluke, a Python package designed to simplify the development of new FL algorithms. fluke is specifically designed for prototyping purposes and is meant for researchers or practitioners focusing on the learning components of a federated system. fluke is open-source, and it can be either used out of the box or extended with new algorithms with minimal overhead. 
653 |a Learning curves 
653 |a Algorithms 
653 |a Python 
653 |a Machine learning 
653 |a Federated learning 
653 |a Prototyping 
773 0 |t arXiv.org  |g (Dec 20, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3148683152/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.15728