⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

November 17, 2020Communications of the ACM

Federated learning for privacy-preserving AI

Yong Cheng Yang Liu Tianjian Chen Qiang Yang

View at Publisher

Ask AI

Bookmark

Share

No abstract yet

We only have this paper's title and publication details.

Read it at the source→

Abstract

Engineering and algorithmic framework to ensure data privacy and user confidentiality.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Cheng et al. (2020) studied this question.

synapsesocial.com/papers/69da2abdb48bb130d46849db https://doi.org/10.1145/3387107

View at Publisher

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 related papers. Consider them for comparative context:

1How Immigration and Federal Funding Sustain American AI LeadershipCommunications of the ACM · Tue,
2A New Era for Inside RisksCommunications of the ACM · Tue,
3Considering AI’s Dependence On Human AgencyCommunications of the ACM · Mon,
4Teaching Novice Computing and Programming in the Agentic AI EraCommunications of the ACM · Mon,
5Is Recursive Self-Improvement Really Here?Communications of the ACM · Fri,

More from Communications of the ACM

Tailoring Elective Courses to Enhance Women’s Participation in ComputingThu,
Scalable and Personalized Oral Assessments Using Voice AITue,
Web Scraping as Illegal Circumvention?Wed,
Working on AI and Technology Policy in the U.S. SenateWed,
‘MVPs Considered Harmful’ Considered HarmfulThu,