NEvoFed: A Decentralized Approach to Federated NeuroEvolution of Heterogeneous Neural Networks-Reference-Cited by-同舟云学术

NEvoFed: A Decentralized Approach to Federated NeuroEvolution of Heterogeneous Neural Networks

Published:2024-07-14 Issue: Volume:35 Page:295-303
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
language:
Short-container-title:

Author:

Custode Leonardo Lucio¹^ORCID,De Falco Ivanoe²^ORCID,Della Cioppa Antonio³⁴^ORCID,Iacca Giovanni¹^ORCID,Scafuri Umberto²^ORCID

Affiliation:

1. Department of Information Engineering and Computer Science, University of Trento, Trento, Italy

2. Institute for High-Performance Computing and Networking, National Research Council of Italy, Napoli, Italy

3. Natural Computation Lab - DIEM, University of Salerno, Salerno, Italy

4. Institute for High-Performance Computing and Networking, National Research Council of Italy, Salerno, Italy

Funder

European Union

PNRR

Publisher

ACM

Reference46 articles.

1. Agarwal, Mayank and Yurochkin, Mikhail and Sun, Yuekai. 2022. Personalization in Federated Learning. Springer, Cham, Switzerland, 71--98.

2. Alam, Samiul and Liu, Luyang and Yan, Ming and Zhang, Mi. 2022. FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction. In Advances in Neural Information Processing Systems, Vol. 35. Neural Information Processing Systems Foundation, San Diego, CA, USA, 29677--29690.

3. Alan McIntyre and Matt Kallada and Cesar G. Miguel and Carolina Feher da Silva. 2019. NEAT-Python. https://github.com/CodeReclaimers/neat-python.

4. Alan McIntyre and Matt Kallada and Cesar G. Miguel and Carolina Feher da Silva. 2020. NEAT-Python Documentation: Configuration file description. https://neat-python.readthedocs.io/en/latest/config_file.html