Let Them Drop: Scalable and Efficient Federated Learning Solutions Agnostic to Stragglers-Reference-Cited by-同舟云学术

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Let Them Drop: Scalable and Efficient Federated Learning Solutions Agnostic to Stragglers

Published:2024-07-30 Issue: Volume:24 Page:1-12
ISSN:
Container-title:Proceedings of the 19th International Conference on Availability, Reliability and Security
language:
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Author:

Taiello Riccardo¹^ORCID,Önen Melek²^ORCID,Gritti Clémentine³^ORCID,Lorenzi Marco⁴^ORCID

Affiliation:

1. Epione Research Group, Inria Sophia Antipolis, France and Digital Security, EURECOM, France and Université Côte d'Azur, France

2. Digital Security, EURECOM, France

3. Digital Security, EURECOM, France and CITI, INSA, France

4. Epione Research Group, Inria Sophia Antipolis, France and Université Côte d'Azur, France

Funder

National Research Agency, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3664476.3664488

Reference34 articles.

1. Secure Single-Server Aggregation with (Poly)Logarithmic Overhead

2. Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, Brendan McMahan, 2019. Towards federated learning at scale: System design. Proceedings of machine learning and systems 1 (2019), 374–388.

3. Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečnỳ, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar. 2018. Leaf: A benchmark for federated settings. arXiv preprint arXiv:1812.01097 (2018).

4. Diogenes: Lightweight Scalable RSA Modulus Generation with a Dishonest Majority

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