REFL: Resource-Efficient Federated Learning-Reference-Cited by-同舟云学术

REFL: Resource-Efficient Federated Learning

Published:2023-05-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Eighteenth European Conference on Computer Systems
language:
Short-container-title:

Author:

Abdelmoniem Ahmed M.¹^ORCID,Sahu Atal Narayan²^ORCID,Canini Marco²^ORCID,Fahmy Suhaib A.²^ORCID

Affiliation:

1. Queen Mary University of London, London, United Kingdom

2. KAUST, Thuwal, Saudi Arabia

Funder

King Abdullah University of Science and Technology (KAUST) Office of Research Ad- ministration (ORA)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3552326.3567485

Reference69 articles.

1. Ahmed M. Abdelmoniem and Marco Canini . 2021 . Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization. In EuroMLSys . Ahmed M. Abdelmoniem and Marco Canini. 2021. Towards Mitigating Device Heterogeneity in Federated Learning via Adaptive Model Quantization. In EuroMLSys.

2. Ahmed M. Abdelmoniem Chen-Yu Ho Pantelis Papageorgiou and Marco Canini. 2022. Empirical Analysis of Federated Learning in Heterogeneous Environments. In EuroMLSys. Ahmed M. Abdelmoniem Chen-Yu Ho Pantelis Papageorgiou and Marco Canini. 2022. Empirical Analysis of Federated Learning in Heterogeneous Environments. In EuroMLSys.

3. Eugene Bagdasaryan Andreas Veit Yiqing Hua Deborah Estrin and Vitaly Shmatikov. 2020. How To Backdoor Federated Learning. In AISTATS. Eugene Bagdasaryan Andreas Veit Yiqing Hua Deborah Estrin and Vitaly Shmatikov. 2020. How To Backdoor Federated Learning. In AISTATS.

4. AI Benchmark. 2021. Performance Ranking. https://ai-benchmark.com/ranking.html AI Benchmark. 2021. Performance Ranking. https://ai-benchmark.com/ranking.html

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