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Federated learning (FL) provides a distributed learning framework formultiple participants to collaborate learning without sharing raw data. In manypractical FL scenarios, participants have heterogeneous resources due todisparities in hardware and inference dynamics that require quickly loadingmodels of different sizes and levels of robustness. The heterogeneity anddynamics together impose significant challenges to existing FL approaches andthus greatly limit FL's applicability. In this paper, we propose a novelSplit-Mix FL strategy for heterogeneous participants that, once training isdone, provides in-situ customization of model sizes and robustness.Specifically, we achieve customization by learning a set of base sub-networksof different sizes and robustness levels, which are later aggregated on-demandaccording to inference requirements. This split-mix strategy achievescustomization with high efficiency in communication, storage, and inference.Extensive experiments demonstrate that our method provides better in-situcustomization than the existing heterogeneous-architecture FL methods. Codesand pre-trained models are available: https://github.com/illidanlab/SplitMix.

Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization