  In federated learning (FL), accommodating clients' varied computational
capacities poses a challenge, often limiting the participation of those with
constrained resources in global model training. To address this issue, the
concept of model heterogeneity through submodel extraction has emerged,
offering a tailored solution that aligns the model's complexity with each
client's computational capacity. In this work, we propose Federated
Importance-Aware Submodel Extraction (FIARSE), a novel approach that
dynamically adjusts submodels based on the importance of model parameters,
thereby overcoming the limitations of previous static and dynamic submodel
extraction methods. Compared to existing works, the proposed method offers a
theoretical foundation for the submodel extraction and eliminates the need for
additional information beyond the model parameters themselves to determine
parameter importance, significantly reducing the overhead on clients. Extensive
experiments are conducted on various datasets to showcase superior performance
of the proposed FIARSE.
