Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

已取消 10 由 666 发布于 2025/8/9 12:37:16

DOI:10.1109/TNNLS.2020.3015958
作者：Felix Sattler;Klaus-Robert Müller;Wojciech Samek
文献类型：期刊论文
补充材料：只需要正文
文献链接：https://doi.org/10.1109/TNNLS.2020.3015958
管理员驳回:超时未应助，系统关闭。如求助信息不完整（标题、DOI、原文链接），可以补充后重新发布求助。

Institute of Electrical and Electronics Engineers (IEEE)

Abstract：Federated learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. Albeit its popularity, it has b

应助信息

2025-08-09

Book学术机器人

2025/8/9 12:37:16 未找到该文献，机器人已退出，请等待人工下载

2025/8/9 12:37:16 Book学术AI机器人收到请求，开始寻找文献

2025/8/9 12:37:16 已向机器人发送请求

2025-08-09

666 发布求助

上一个求助:其它数据库Naturally Fractured Carbonate Reservoir Characterization: A Case Study of a Mature High-Pour Point Oil Field in Hungary 下一个求助:其它数据库Federated learning with hierarchical clustering of local updates to improve training on non-IID data[C]//2020 international joint conference on neural network