{"title":"基于特征的数据集指纹识别，用于医学图像数据的聚类联合学习","authors":"Daniel Scheliga, Patrick Mäder, Marco Seeland","doi":"10.1080/08839514.2024.2394756","DOIUrl":null,"url":null,"abstract":"Federated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility bec...","PeriodicalId":8260,"journal":{"name":"Applied Artificial Intelligence","volume":"315 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data\",\"authors\":\"Daniel Scheliga, Patrick Mäder, Marco Seeland\",\"doi\":\"10.1080/08839514.2024.2394756\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Federated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility bec...\",\"PeriodicalId\":8260,\"journal\":{\"name\":\"Applied Artificial Intelligence\",\"volume\":\"315 1\",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1080/08839514.2024.2394756\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/08839514.2024.2394756","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data
Federated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility bec...
期刊介绍：
Applied Artificial Intelligence addresses concerns in applied research and applications of artificial intelligence (AI). The journal also acts as a medium for exchanging ideas and thoughts about impacts of AI research. Articles highlight advances in uses of AI systems for solving tasks in management, industry, engineering, administration, and education; evaluations of existing AI systems and tools, emphasizing comparative studies and user experiences; and the economic, social, and cultural impacts of AI. Papers on key applications, highlighting methods, time schedules, person-months needed, and other relevant material are welcome.
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