Data source selection in federated learning: A submodular optimization approach

• Main Authors: ZHANG, Ruisheng, WANG, Yansheng, ZHOU, Zimu, REN, Ziyao, TONG, Yongxin, XU, Ke
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Federated learning; Data source selection; Submodularity; Databases and Information Systems; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7219; https://ink.library.smu.edu.sg/context/sis_research/article/8222/viewcontent/dasfaa22_zhang.pdf
• Institution: Singapore Management University

Federated learning is a new learning paradigm that jointly trains a model from multiple data sources without sharing raw data. For the practical deployment of federated learning, data source selection is compulsory due to the limited communication cost and budget in real-world applications. The nece...