{"title":"Federated Bayesian optimization on random Fourier additive margin features and random kernel mapping","authors":"Fazhen Jiang , Xiaoyuan Yang","doi":"10.1016/j.asoc.2025.112925","DOIUrl":null,"url":null,"abstract":"<div><div>Bayesian Optimization (BO) is an advanced technique for hyperparameter tuning in AutoML, particularly for optimizing black-box functions. This study mainly proposes the RAF kernel for Gaussian Processes and introduces two novel algorithms<span><math><mo>:</mo></math></span> the Federated Bayesian additive marginal Thompson Sampling algorithm (FAT) and the Federated Bayesian random kernel Thompson Sampling algorithm (FAKT), the latter combining RAF with Random Fourier Features (RFF). To enhance privacy, we further develop DP-FAT and DP-FAKT by integrating Differential Privacy, which can reduce the communication costs while safeguarding client data. Experiments show that FAT and FAKT converge 10 communication rounds faster than existing methods (e.g., FTS), significantly improving efficiency in federated black-box optimization. These advancements demonstrate strong potential for large-scale learning tasks with enhanced privacy and reduced overhead.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 112925"},"PeriodicalIF":7.2000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494625002364","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Bayesian Optimization (BO) is an advanced technique for hyperparameter tuning in AutoML, particularly for optimizing black-box functions. This study mainly proposes the RAF kernel for Gaussian Processes and introduces two novel algorithms the Federated Bayesian additive marginal Thompson Sampling algorithm (FAT) and the Federated Bayesian random kernel Thompson Sampling algorithm (FAKT), the latter combining RAF with Random Fourier Features (RFF). To enhance privacy, we further develop DP-FAT and DP-FAKT by integrating Differential Privacy, which can reduce the communication costs while safeguarding client data. Experiments show that FAT and FAKT converge 10 communication rounds faster than existing methods (e.g., FTS), significantly improving efficiency in federated black-box optimization. These advancements demonstrate strong potential for large-scale learning tasks with enhanced privacy and reduced overhead.
期刊介绍:
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.