Privacy and Robustness in Federated Learning: Attacks and Defenses-Reference-Cited by-同舟云学术

Privacy and Robustness in Federated Learning: Attacks and Defenses

Author:

Lyu Lingjuan¹^ORCID,Yu Han²^ORCID,Ma Xingjun³,Chen Chen¹^ORCID,Sun Lichao⁴,Zhao Jun²^ORCID,Yang Qiang⁵,Yu Philip S.⁶^ORCID

Affiliation:

1. Sony AI, Tokyo, Japan

2. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

3. School of Computer Science, Fudan University, Shanghai, China

4. Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA

5. Department of Artificial Intelligence (AI), WeBank, China

6. Department of Computer Science, University of Illinois Chicago, Chicago, IL, USA

Funder

Sony AI

Joint NTU-WeBank Research Centre on Fintech

Nanyang Technological University

Joint SDU-NTU Centre for Artificial Intelligence Research

National Research Foundation Singapore

RIE 2020 Advanced Manufacturing and Engineering (AME) Programmatic Fund, Singapore

Nanyang Technological University through the Nanyang Assistant Professorship

Future Communications Research & Development Programme

Hong Kong RGC Theme-Based Research Scheme

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

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