.png){kind=icon}
返回
Enhancing Federated Learning With Pattern-Based Client Clustering
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2024.3452717.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The exponential growth of the Internet of Things (IoT), driven by the increasing number of connected devices and sensors, is profoundly transforming many industries. This proliferation has been enabled by advances in storage and processing capabilities, facilitating the implementation of deep model. However, concerns surrounding privacy and substantial bandwidth required for data transmission arise from the centralized processing of data collected from numerous distributed devices. To address these issues, federated learning (FL) has emerged as a viable solution with a decentralized strategy. It ensures data privacy by allowing devices to locally train models while keeping their data localized, thus reducing centralization needs and concerns related to privacy and bandwidth. To mitigate the data heterogeneity problem in FL scenarios, we propose federated pattern extraction clustering (FedPEC) to cluster clients with similar data distributions. Compare to existing client clustering methods, FedPEC requires no additional data transmission and ensures great flexibility, scalability and privacy. Furthermore, we discuss the discrepancy between representation and data distribution in existing methods from the perspective of pattern representation. Based on this, we propose a variant of FedPEC as a solution, which has achieved excellent performance on multiple FL data sets.
Keyword:
Data models
Computational modeling
Internet of Things
Federated learning
Data privacy
Servers
Training
Client clustering
distributed learning
federated learning (FL)
pattern representation
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
Alaskan Upper Miocene Marine Glacial Deposits and the
Turborotalia pachyderma
Datum Plane.png)
Science
ScienceIF0
PREAI
分享收藏Intercellular Adhesion Molecule 1 (ICAM-1) Gene Variant is Associated with Coronary Artery Calcification Independent of Soluble ICAM-1 Levels细胞间粘附分子1 (ICAM-1) 基因变异与冠状动脉钙化相关，与可溶性ICAM-1水平无关
PREAI
分享收藏The Revised Speech Perception in Noise Test (R-SPIN) in a Multiple Signal-to-Noise Ratio Paradigm
PREAI
分享收藏A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI可解释人工智能 (XAI) 调查: 走向医学XAI
OAAI
OAAI分享收藏
查看更多内容