Sharing non-reversible data statistics for fast and secure Federated learning native to Extreme Learning Machine-Reference-Cited by-同舟云学术

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Sharing non-reversible data statistics for fast and secure Federated learning native to Extreme Learning Machine

Published:2024-06-26 Issue: Volume: Page:674-675
ISSN:
Container-title:Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments
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Author:

Akusok Anton¹^ORCID,Espinosa-Leal Leonardo¹^ORCID,BjöRk Kaj-Mikael²^ORCID

Affiliation:

1. Arcada University of Applied Sciences, Finland

2. Arcada University of Applied Sciences, Finland and Director, Centre For Intelligent Computing (CIC.UTU.FI), Finland

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3652037.3663891

Reference8 articles.

1. Anton Akusok. 2024. Federated ELM. https://github.com/akusok/federated-elm/

2. High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications

3. Anton Akusok, Leonardo Espinosa-Leal, Tamirat Atsemegiorgis, and Kaj-Mikael Björk. 2024. Data Obfuscation Scenarios for Batch ELM in Federated Learning Applications. In STE2024: 21st International Conference on Smart Technologies & Education.

4. Scikit-ELM: An Extreme Learning Machine Toolbox for Dynamic and Scalable Learning

5. Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Hei Li Kwing, Titouan Parcollet, Pedro PB de Gusmão, and Nicholas D Lane. 2020. Flower: A Friendly Federated Learning Research Framework. arXiv preprint arXiv:2007.14390 (2020).

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