FLScalize: Federated Learning Lifecycle Management Platform
Author:
Affiliation:
1. Department of Computer Engineering, Gachon University, Seongnam-si, South Korea
2. Innopia Technologies Inc., Seongnam-si, South Korea
Funder
Commercializations Promotion Agency for Research and Development Outcome
Korean Government (MSIT) through the Future Research Service Development Support
National Research Foundation of Korea (NRF) Grant funded by MSIT
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10122960.pdf?arnumber=10122960
Reference29 articles.
1. A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
2. MLOps scaling machine learning lifecycle in an industrial setting;zhao;Int J Ind Manuf Eng,2022
3. Adaptive federated optimization;reddi;Proc ICLR,2021
4. Federated optimization in heterogeneous networks;li;Proc Mach Learn Syst,2020
5. Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?
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