An effective and efficient green federated learning method for one-layer neural networks

Author:

Fontenla-Romero Oscar1ORCID,Guijarro-Berdiñas Berta1ORCID,Hernandez-Pereira Elena1ORCID,Perez-Sanchez Beatriz1ORCID

Affiliation:

1. University of A Coruña, CITIC, A Coruña, Spain

Funder

Agencia estatal de investigación. Gobierno de España

Xunta de Galicia

Publisher

ACM

Reference5 articles.

1. D. Dua and C. Graff. 2017. UCI Machine Learning Repository. https://archive.ics.uci.edu/ml University of California Irvine School of Information and Computer Sciences.

2. O. Fontenla-Romero B. Guijarro-Berdiñas E. Hernández-Pereira and B. Pérez-Sánchez. 2023. An effective and efficient green federated learning method for one-layer neural networks. arXiv:2312.14528 [cs.LG]

3. FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks

4. H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Vol. 54. PMLR, 1273--1282.

5. C. J. Wu, R. Raghavendra, U. Gupta, B. Acun, N. Ardalani, K. Maeng, G. Chang, F. Behram, J. Huang, C. Bai, M. Gschwind, A. Gupta, M. Ott, A. Melnikov, S. Candido, D. Brooks, G. Chauhan, B. Lee, H-H.S. Lee, B. Akyildiz, M. Balandat, J. Spisak, R. Jain, M. Rabbat, and K. Hazelwood. 2021. Sustainable AI: Environmental Implications, Challenges and Opportunities. CoRR (2021), 2111.00364.

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1. An effective and efficient green federated learning method for one-layer neural networks;Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing;2024-04-08

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