Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data

IF 2.9 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Artificial Intelligence Pub Date : 2024-09-09 DOI:10.1080/08839514.2024.2394756

Daniel Scheliga, Patrick Mäder, Marco Seeland

引用次数: 0

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...

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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

联合学习（FL）允许多个客户在不共享私人训练数据的情况下训练一个共同的模型。在实践中，联合优化难以实现次优模型效用，因为...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

5.20

自引率

3.60%

发文量

106

审稿时长

6 months

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.