计算机科学
数据库扫描
聚类分析
适应性
GSM演进的增强数据速率
边缘计算
数据挖掘
人工智能
机器学习
分布式计算
相关聚类
生态学
生物
树冠聚类算法
作者
Wei-Che Chien,Chih-Hsun Lin,Tianli Zhu,Cheng Dai,Sahil Garg,Amrit Mukherjee
标识
DOI:10.1109/jiot.2024.3522164
摘要
This research introduces the Density-Clustering based Aggregation for Personalized Federated Learning (DCPFL) algorithm, which utilizes DBSCAN clustering to enhance model accuracy in AI-enabled aerial and edge computing contexts, particularly for UAVs. The DCPFL framework promotes model sharing among clients, fostering the development of personalized and optimized models. DBSCAN is beneficial in automatically determining cluster numbers using EPS neighborhoods and MinPts, with parameter optimization achieved through cross-experimental analysis. We further refined the model exchange mechanism by integrating a moving average prediction model to optimize the timing of these exchanges. Tests conducted on three public datasets covering two different machine learning tasks show that DCPFL surpasses existing methods, offering greater accuracy and enhanced adaptability in varied data environments. Implementing this algorithm in UAV networks leverages AI capabilities in aerial and edge computing to efficiently balance personalized modeling requirements with high performance, showcasing its potential to push federated learning forward in complex and dynamic settings.
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