  Although federated learning (FL) has recently been proposed for efficient
distributed training and data privacy protection, it still encounters many
obstacles. One of these is the naturally existing statistical heterogeneity
among clients, making local data distributions non independently and
identically distributed (i.e., non-iid), which poses challenges for model
aggregation and personalization. For FL with a deep neural network (DNN),
privatizing some layers is a simple yet effective solution for non-iid
problems. However, which layers should we privatize to facilitate the learning
process? Do different categories of non-iid scenes have preferred privatization
ways? Can we automatically learn the most appropriate privatization way during
FL? In this paper, we answer these questions via abundant experimental studies
on several FL benchmarks. First, we present the detailed statistics of these
benchmarks and categorize them into covariate and label shift non-iid scenes.
Then, we investigate both coarse-grained and fine-grained network splits and
explore whether the preferred privatization ways have any potential relations
to the specific category of a non-iid scene. Our findings are exciting, e.g.,
privatizing the base layers could boost the performances even in label shift
non-iid scenes, which are inconsistent with some natural conjectures. We also
find that none of these privatization ways could improve the performances on
the Shakespeare benchmark, and we guess that Shakespeare may not be a seriously
non-iid scene. Finally, we propose several approaches to automatically learn
where to aggregate via cross-stitch, soft attention, and hard selection. We
advocate the proposed methods could serve as a preliminary try to explore where
to privatize for a novel non-iid scene.
