  Federated learning (FL) provides a distributed learning framework for
multiple participants to collaborate learning without sharing raw data. In many
practical FL scenarios, participants have heterogeneous resources due to
disparities in hardware and inference dynamics that require quickly loading
models of different sizes and levels of robustness. The heterogeneity and
dynamics together impose significant challenges to existing FL approaches and
thus greatly limit FL's applicability. In this paper, we propose a novel
Split-Mix FL strategy for heterogeneous participants that, once training is
done, provides in-situ customization of model sizes and robustness.
Specifically, we achieve customization by learning a set of base sub-networks
of different sizes and robustness levels, which are later aggregated on-demand
according to inference requirements. This split-mix strategy achieves
customization with high efficiency in communication, storage, and inference.
Extensive experiments demonstrate that our method provides better in-situ
customization than the existing heterogeneous-architecture FL methods. Codes
and pre-trained models are available: https://github.com/illidanlab/SplitMix.
