The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Scaling Distributed Machine Learning with the Parameter Server
Open accessAbstract
Big data may contain big values, but also brings lots of challenges to the computing theory, architecture, framework, knowledge discovery algorithms, and domain specific tools and applications. Beyond the 4-V or 5-V characters of big datasets, the data processing shows the features like inexact, incremental, and inductive manner. This brings new research opportunities to research community across theory, systems, algorithms, and applications. Is there some new "theory" for the big data? How to handle the data computing algorithms in an operatable manner? This report shares some view on new challenges identified, and covers some of the application scenarios such as micro-blog data analysis and data processing in building next generation search engines.
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Cites
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Cited by
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Wang, S., Tuor, T., Salonidis, T., Leung, K. K., Makaya, C., He, T., & Chan, K. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904348 · Cited by 2,232
A Survey on Distributed Machine Learning
Verbraeken, J., Wolting, M., Katzy, J., Kloppenburg, J., Verbelen, T., & Rellermeyer, J. S. (2021) · ACM Computing Surveys · doi:10.1145/3377454 · Cited by 872
Speeding Up Distributed Machine Learning Using Codes
Lee, K., Lam, M., Pedarsani, R., Papailiopoulos, D., & Ramchandran, K. (2018) · IEEE Transactions on Information Theory · doi:10.1109/tit.2017.2736066 · Cited by 829
An Efficient Framework for Clustered Federated Learning
Ghosh, A., Chung, J., Yin, D., & Ramchandran, K. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3192506 · Cited by 623
Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis
Ben-Nun, T., & Hoefler, T. (2020) · ACM Computing Surveys · doi:10.1145/3320060 · Cited by 577
Metadata
- Authors
- Li, M.
- Venue
- Proceedings of the 2014 International Conference on Big Data Science and Computing
- Publisher
- ACM
- Published
- 2014-08-04
- Pages
- 1-1
- OpenAlex
- W2060393849
- Cited by
- 979
- References
- 33
- Type
- conference abstract
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
