Cryptography-Inspired Federated Learning for Generative Adversarial Networks and Meta Learning

Yu Zheng; Wei Song; Minxin Du; Sherman S. M. Chow; Qian Lou; Yongjun Zhao; Xiuhua Wang

doi:10.1007/978-3-031-46664-9_27

ScienceGate Book Chapters

BOOK-CHAPTER

Cryptography-Inspired Federated Learning for Generative Adversarial Networks and Meta Learning

Yu Zheng Wei Song Minxin Du Sherman S. M. Chow Qian Lou Yongjun Zhao Xiuhua Wang

Year: 2023 Lecture notes in computer science Pages: 393-407 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-46664-9_27

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Adversarial system Generative grammar Artificial intelligence Theoretical computer science Cryptography Machine learning Computer security

Metrics

Cited By

4.55

FWCI (Field Weighted Citation Impact)

Refs

0.95

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Privacy-Preserving Technologies in Data

Physical Sciences → Computer Science → Artificial Intelligence

Cryptography and Data Security

Physical Sciences → Computer Science → Artificial Intelligence

Wireless Communication Security Techniques

Physical Sciences → Engineering → Electrical and Electronic Engineering

Cryptography-Inspired Federated Learning for Generative Adversarial Networks and Meta Learning

Metrics

Citation History

Topics

Related Documents

Generative Adversarial Networks in Federated Learning

Federated Generative Adversarial Learning

Exploring Generative Adversarial Networks and Meta-Learning

A Novel Federated Learning Scheme for Generative Adversarial Networks

PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks