  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 \textit{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
\textit{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.
