Secure Federated Evolutionary Optimization-A Survey

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外文摘要：With the development of edge devices and cloud computing,the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past decade.As a privacy-preserving distributed machine learning method,federated learning(FL)has become popular in the last few years.However,the data privacy issue also occurs when solving optimization problems,which has received little attention so far.This survey paper is concerned with privacy-preserving optimization,with a focus on privacy-preserving data-driven evolutionary optimiza-tion.It aims to provide a roadmap from secure privacy-preserving learning to secure privacy-preserving optimization by summarizing security mechanisms and privacy-preserving approaches that can be employed in machine learning and optimization.We provide a formal definition of security and privacy in learning,followed by a comprehensive review of FL schemes and cryptographic privacy-preserving techniques.Then,we present ideas on the emerging area of privacy-preserving optimization,ranging from privacy-preserving distributed optimization to privacy-preserving evolutionary optimization and privacy-preserving Bayesian optimization(BO).We further provide a thorough security analysis of BO and evolutionary optimization methods from the perspective of inferring attacks and active attacks.On the basis of the above,an in-depth discussion is given to analyze what FL and distributed optimization strategies can be used for the design of federated optimization and what additional requirements are needed for achieving these strategies.Finally,we conclude the survey by outlining open questions and remaining challenges in federated data-driven optimization.We hope this survey can provide insights into the relationship between FL and federated optimization and will promote research interest in secure federated optimization.

外文关键词：

Federated learningPrivacy-preservationSecurityEvolutionary optimizationData-driven optimizationBayesian optimization

作者：

Qiqi Liu、Yuping Yan、Yaochu Jin、Xilu Wang、Peter Ligeti、Guo Yu、Xueming Yan

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作者单位：

School of Engineering,WestLake University,Hangzhou 310030,China

Faculty of Informatics,Department of Computer Algebra,Eötvös Lorúnd University,Budapest 1053,Hungury

Faculty of Technology,Bielefeld University,Bielefeld 33619,Germany

Institute of Intelligent Manufacturing,Nanjing Tech University,Nanjing 211816,Chtna

School of Information Science and Technology,Guangdong University of Foreign Studies,Guangzhou 510006,China

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基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaChina Postdoctoral Science FoundationNatural Science Foundation of Guangdong ProvinceNational Research,Development and Innovation Fund of Hungary under the Establishment of Competence Centers,Development of Rean European Research Council(ERC)Advanced Grant"ERMiD"Alexander von Humboldt Professorship for Artificial Intelligence awarded by the German Federal Ministry of Education and Researc

项目编号：

62136003623021476210315062006053623060972021M6910122022A15150104432019-13.1-KK-2019-00011

出版年：

2024

DOI：

10.1016/j.eng.2023.10.006

工程(英文)

CSTPCDEI

ISSN：2095-8099

年,卷(期)：2024.34(3)