计算机科学
独立同分布随机变量
趋同(经济学)
GSM演进的增强数据速率
原始数据
资源(消歧)
点(几何)
比例(比率)
机器学习
人工智能
数据挖掘
统计
数学
随机变量
计算机网络
地理
几何学
地图学
经济
程序设计语言
经济增长
作者
Jiachen Liu,Liuping Fan,Yinwei Dai,Aditya Akella,Harsha V. Madhyastha,Mosharaf Chowdhury
标识
DOI:10.1145/3620678.3624651
摘要
Federated learning (FL) is an emerging machine learning (ML) paradigm that enables heterogeneous edge devices to collaboratively train ML models without revealing their raw data to a logically centralized server. However, beyond the heterogeneous device capacity, FL participants often exhibit differences in their data distributions, which are not independent and identically distributed (Non-IID). Many existing works present point solutions to address issues like slow convergence, low final accuracy, and bias in FL, all stemming from client heterogeneity. In this paper, we explore an additional layer of complexity to mitigate such heterogeneity by grouping clients with statistically similar data distributions (cohorts). We propose Auxo to gradually identify such cohorts in large-scale, low-availability, and resource-constrained FL populations. Auxo then adaptively determines how to train cohort-specific models in order to achieve better model performance and ensure resource efficiency. Our extensive evaluations show that, by identifying cohorts with smaller heterogeneity and performing efficient cohort-based training, Auxo boosts various existing FL solutions in terms of final accuracy (2.1% - 8.2%), convergence time (up to 2.2x), and model bias (4.8% - 53.8%).
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