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Cited by 726Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot LearningStochastic Gradient Optimization Techniques

Distributed learning of deep neural network over multiple agents

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Gupta, Otkrist, Raskar, Ramesh·2018·Journal of Network and Computer Applications
DOI 10.1016/j.jnca.2018.05.003
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  • The field of secure multi-party computation has moved from being of purely theoretical interest to a deployed technology in industry.

    “Thanks to both theoretical and engineering breakthroughs, the field has moved from being of a purely theoretical interest to a deployed technology in industry [78, 77, 295, 27, 191, 242, 243].”

    From Advances and Open Problems in Federated Learning (2021) · cites this paper as [78]

    The Related Work section discusses secure computation and notes that some methods are impractical at scale. The Datasets and Implementation section describes the authors’ own protocol, but no passage found says that secure multi-party computation has progressed from theoretical interest to deployment in industry (searched for that transition and industrial applications). Page numbers are unavailable in our copy. (LLM, conf=0.73)

    Experimental — accuracy not yet measured

  • Split DL provides a flexible way to train a DNN by dividing it into lower and upper segments located at the edge device-side and edge server-side, respectively.

    “Split DL further provides a flexible way to train a DNN by dividing it into lower and upper segments located at the edge device-side and edge server-side, respectively [36].”

    From Edge artificial intelligence for 6g: vision, enabling technologies, and applications (2022) · cites this paper as [36]

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Research works printed but not linked: 36

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Cited by

Sorted by citations

Authors
Gupta, O., & Raskar, R.
Affiliations
Massachusetts Institute of Technology (US)
Corresponding author
Gupta, Otkrist
ORCID
Venue
Journal of Network and Computer Applications
Publisher
Elsevier BV
Published
2018-08
Volume
116
Pages
1-8
ISSN
1084-8045 (ISSN-L)
DOI
10.1016/j.jnca.2018.05.003
OpenAlex
W2803823081
Cited by
726
References
2
Citations by year
2026: 84 · 2025: 142 · 2024: 149 · 2023: 140 · 2022: 93 · 2021: 66 · 2020: 38 · 2019: 10 · 2018: 4
FWCI
16.61
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Network and Computer Applications (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
Keywords
distributed deep learning, multi-agent learning, deep neural network, data scarcity, semi-supervised learning, few labeled samples, privacy-preserving training, data sharing

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