文献类型：期刊文献

英文题名：A federated data-driven evolutionary algorithm

作者：Xu, Jinjin[1];Jin, Yaochu[1,2];Du, Wenli[1];Gu, Sai[3]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Surrey, Dept Comp Sci, Guildford GU2 7XH, Surrey, England;[3]Univ Surrey, Dept Chem Proc Engn, Guildford GU2 7XH, Surrey, England

年份：2021

卷号：233

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20214110997681);WOS:【SCI-EXPANDED(收录号:WOS:000709922600005)】；

语种：英文

外文关键词：Data-driven evolutionary optimization; Distributed optimization; Federated learning; RBFN surrogate model

摘要：Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems. However, existing data-driven optimization algorithms require that all data are centrally stored, which is not always practical and may be vulnerable to privacy leakage and security threats if the data must be collected from different devices. To address the above issue, this paper proposes a federated data-driven evolutionary optimization framework that is able to perform data driven optimization when the data is distributed on multiple devices. On the basis of federated learning, a sorted model aggregation method is developed for aggregating local surrogates based on radial-basis-function networks. In addition, a federated surrogate management strategy is suggested by designing an acquisition function that takes into account the information of both the global and local surrogate models. Empirical studies on a set of widely used benchmark functions in the presence of various data distributions demonstrate the effectiveness of the proposed framework. (c) 2021 Elsevier B.V. All rights reserved.