HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
CoRR(2024)
摘要
Federated Learning (FL) is a practical approach to train deep learning models
collaboratively across user-end devices, protecting user privacy by retaining
raw data on-device. In FL, participating user-end devices are highly fragmented
in terms of hardware and software configurations. Such fragmentation introduces
a new type of data heterogeneity in FL, namely system-induced data
heterogeneity, as each device generates distinct data depending on its
hardware and software configurations. In this paper, we first characterize the
impact of system-induced data heterogeneity on FL model performance. We collect
a dataset using heterogeneous devices with variations across vendors and
performance tiers. By using this dataset, we demonstrate that
system-induced data heterogeneity negatively impacts accuracy, and
deteriorates fairness and domain generalization problems in FL. To address
these challenges, we propose HeteroSwitch, which adaptively adopts
generalization techniques (i.e., ISP transformation and SWAD) depending on the
level of bias caused by varying HW and SW configurations. In our evaluation
with a realistic FL dataset (FLAIR), HeteroSwitch reduces the variance of
averaged precision by 6.3% across device types.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要