Personalized Federated Learning With Differential Privacy and Convergence Guarantee-Reference-Cited by-同舟云学术

Personalized Federated Learning With Differential Privacy and Convergence Guarantee

Author:

Wei Kang¹^ORCID,Li Jun¹^ORCID,Ma Chuan²^ORCID,Ding Ming³^ORCID,Chen Wen⁴^ORCID,Wu Jun⁵^ORCID,Tao Meixia⁴^ORCID,Poor H. Vincent⁶^ORCID

Affiliation:

1. School of Electrical and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China

2. Zhejiang Laboratory, Hangzhou, China

3. Data61, CSIRO, Sydney, NSW, Australia

4. Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China

5. Shanghai Key Laboratory of Intelligent Information Processing and the School of Computer Science, Fudan University, Shanghai, China

6. Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Future Network Grant of Provincial Education Board in Jiangsu

Youth Foundation Project through the Zhejiang Laboratory

National Key Project

Shanghai

U.S. National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

Reference41 articles.

1. Exploiting shared representations for personalized federated learning;collins;Proc Int Conf Mach Learn (ICML),2021

3. Parameterized knowledge transfer for personalized federated learning;zhang;Proc Adv Neural Inf Process Syst (NeurIPS),2021

4. Deep learning with differential privacy;martin;Proc ACM Conf Comput Commun Secur (CCS),2016

5. Federated meta-learning with fast convergence and efficient communication;chen;arXiv 1802 07876,2018

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